Language：English   Publishing type：Research paper (scientific journal)  

In recent years, due to breeding improvements, strawberries with low anthocyanin content and a white rind are now available, and they are highly valued in the market. Strawberries with white skin color do not turn red when ripe, making it difficult to judge ripeness. The soluble solids content (SSC) is an indicator of fruit quality and is closely related to ripeness. In this study, visible–near-infrared (Vis-NIR) spectroscopy and near-infrared (NIR) spectroscopy are used for non-destructive evaluation of the SSC. Vis-NIR (500–978 nm) and NIR (908–1676 nm) data collected from 180 samples of “Tochigi iW1 go” white strawberries and 150 samples of “Tochigi i27 go” red strawberries are investigated. The white strawberry SSC model developed by partial least squares regression (PLSR) in Vis-NIR had a determination coefficient R2p of 0.89 and a root mean square error prediction (RMSEP) of 0.40%; the model developed in NIR showed satisfactory estimation accuracy with an R2p of 0.85 and an RMSEP of 0.43%. These estimation accuracies were comparable to the results of the red strawberry model. Absorption derived from anthocyanin and chlorophyll pigments in white strawberries was observed in the Vis-NIR region. In addition, a dataset consisting of red and white strawberries can be used to predict the pigment-independent SSC. These results contribute to the development of methods for a rapid fruit sorting system and the development of an on-site ripeness determination system.

DOI： 10.3390/foods13142274

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Language：English   Publishing type：Research paper (scientific journal)  

In recent years, the focus has shifted towards carbohydrate-based hydrogels and their eco-friendly preparation methods. This study involved an investigation into the treatment of wheat starch using dielectric barrier discharge (DBD) plasma technology over varying time gradients (0, 2, 5, 10, 15, and 20 min). The objective was to systematically examine the impact of different treatment durations on the physicochemical properties of wheat starch and the suitability of its gels for 3D printing. Morphology of wheat starch remained intact after DBD treatment. However, it led to a reduction in the amylose content, molecular weight, and crystallinity. This subsequently resulted in a decrease in the pasting temperature and viscosity. Moreover, the gels of the DBD-treated starch exhibited superior 3D printing performance. After a 2-min DBD treatment, the 3D printed samples of the wheat starch gel showed no significant improvements, as broken bars were evident on the surface of the 3D printed graphic, whereas DBD-20 showed better printing accuracy and surface structure, compared to the original starch without slumping. These results suggested that DBD technology holds potential for developing new starch-based gels with impressive 3D printing properties.

DOI： 10.1016/j.ijbiomac.2024.132159

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)  

The decay of fluorescence lifetime (FLT) is an absolute indication for observing the environment surrounding the fluorescent molecules. This study presents the characterization of fluorescence intensity and the FLT in tomatoes, spanning from the green to the red stage, using a novel time-resolved and laser-induced fluorescence (TRLIF) spectroscopic system. Furthermore, the applicability of this method was assessed for evaluating the postharvest quality of commercially acquired red tomatoes. The results collectively indicate a progressive decrease in the FLT of the red tomatoes throughout shelf-life storage. The average FLT loss decreased by approximately 17, 26, 32, and 38% after 1, 2, 3, and 4 d, respectively. Furthermore, the first principal component scores of the time-resolved spectra were confirmed to be stable for early quality degradation monitoring purposes. This investigation highlights that the devised TRLIF spectroscopic technique is a non-destructive and robust method for studying the degradation of fluorescent compounds. It facilitates an in-depth exploration of the underlying mechanisms behind the fluorescent phenomena in postharvest research.

DOI： 10.1016/j.scienta.2024.113059

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Visible-near-infrared (Vis-NIR) spectral imaging holds great promise for the automatic detection of fruit defects. However, uneven brightness resulting from fruit geometry and the limitations of one-directional imaging significantly restrict the current detection to a limited area. This study presents a rotation measurement system that combines a line-scan NIR-hyperspectral imaging (HSI) camera with a laser profile. A total of 72 apple samples with bruises in the central and edge regions were prepared. A 360° scanning approach was employed to collect HSI and shape data from the entire surface of the samples over a 6-h post-bruising period. Height and angle corrections were applied to eliminate the surface geometric influences on the HSI data, resulting in improved reflectance spectrum uniformity. A two-step principal component analysis method was employed for image enhancement, followed by a straightforward bruise detection technique using global segmentation and connected-domain selection. The results demonstrated an overall improvement in bruise detection over time. Moreover, the correction significantly enhanced the detection accuracy. After 6 h of bruising, the corrected data achieved a classification accuracy of 90.3% and an identification rate of 83.3% for central bruises and 61.1% for edge bruises, whereas the uncorrected data yielded 70.8%, 58.3%, and 31.9%, respectively. Thus, this study successfully detected early bruising across the entire surface of apples and improved the detection in low-intensity edge areas. The proposed method has the potential to contribute to the comprehensive evaluation of agricultural products with irregular geometries.

DOI： 10.1016/j.postharvbio.2023.112753

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

In past studies, finite element analysis (FEA) methods have been used to simulate the thermal and moisture coupling of wood. However, challenges remain in achieving high-quality three-dimensional (3D) simulations, mainly because of the heterogeneous and complex structure of wood and its difficult-to-detect internal structure, which makes modeling challenging, in addition to the lack of robust experimental techniques to validate simulation results. In this study, the FEA simulation model was refined by combining X-ray computed tomography (CT) and near-infrared hyperspectral imaging (NIR-HSI). CT was used to probe the 3D density of wood, and a novel FEA tetrahedral mesh was constructed based on the results. The NIR-HSI method visualizes the moisture distribution during adsorption and desorption inside the wood. This result is then used to adjust the parameters of the FEA simulation model and as a reference value to evaluate the simulation results. The visualization and simulation results fit well with the theoretical properties. The simulation results can more accurately reflect the spatial distribution and transfer trend of wood moisture at different points in time. Therefore, the CT and NIR-HSI-based 3D heat and moisture-coupled FEA model of wood proposed in this study can be used as a basis for optimizing drying parameters to provide high-quality wood.

DOI： 10.1186/s10086-023-02120-2

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Farmers would be able to regulate fertilization and produce quality durian if they knew the nutrient concentration in durian leaves. A long period of time for traditional nutritional content determination is needed. Therefore, near-infrared spectroscopy is a good method for nondestructive and quick nutrient content evaluation. The leaf sample matrices (fresh leaves, dried ground leaves, and dried ground leaf pellets) were scanned by Fourier transform near-infrared (FT-NIR) with a wavelength of 12,500–3,600 cm−1. Regression models were developed using partial least squares (PLS) with full wavelength, short wavelength, and selected wavelength by successive projections algorithm (SPA). In this study, the model for N and K concentration was acceptable and the prediction was considered good but for P content not had succeeded. As a result, the PLS-SPA model using fresh leaf samples for evaluating N content in durian leaves exhibited performance of r2 = 0.852, SEP = 0.14%, RPD = 2.63 and bias = −0.020%. The PLS-SPA model using dried ground leaf samples for evaluating K content in durian leaves exhibited performance of r2 = 0.820, SEP = 0.13%, RPD = 2.36 and bias = 0.006%. This research found that it is possible to apply NIR waves to predict N and K concentrations in durian leaves. It is not necessary to predict directly from the wavelengths associated with -N or -K bonds. Instead, NIR can measure them indirectly from the bonding of proteins, which are products formed by N and K. In addition, selecting the wavelength that is related to the value to be measured can produce results that are not significantly different from using full or short wavelengths. These models can assist farmers in rapidly predicting N and K content in durian leaves for immediate fertilizer adjustment.

DOI： 10.1016/j.saa.2023.123398

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Language：English   Publishing type：Research paper (scientific journal)  

Purpose of Review: Forest and wood products are often characterized by a uniformity of quality attributes, which necessitates the development of rapid and non-destructive quality evaluation methods to ensure their optimal quality. Near-infrared spectroscopy (NIRS) represents a highly suitable approach for the characterization of organic compounds, and is generally combined with sophisticated multivariate analysis methods. This review article presents a range of scientific and technical reports showcasing the successful use of NIRS for evaluating forest and wood products, mainly published within the past 5 years. Recent Findings: Continuous advancements in spectral imaging techniques and the integration of big-data analytics have greatly enhanced the capabilities of NIR instrumentation, enabling its widespread application across diverse fields. Although NIR spectral imaging methods do have some limitations when it comes to online grading, they can still be used to test small quantities of samples at a batch level. Moreover, the ever-increasing use of handheld devices has made NIRS easily accessible. Summary: We aim to provide a summary of new research in basic spectroscopic research, integrating the improvements of spectral imaging methods and big-data analytics. Furthermore, low-cost and portable devices have been produced, enabling remote analysis and further expanding the scope of NIRS applications. Looking forward, we anticipate that continued advancements in this field will enable even wider applications of NIRS for online or at-line quality monitoring in diverse fields.

DOI： 10.1007/s40725-023-00203-3

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：TAYLOR & FRANCIS INC  

Hyperspectral imaging is a technique that combines spectroscopy and imaging. Originally utilized in the 1980's by the remote sensing community it is now utilized in a wide variety of applications. Spectral imaging was first used for the detection of compression wood in the late 1990's and since that time research focused on wood and wood products has steadily increased with a variety of applications reported. While there are several reviews of wood related research utilizing near infrared spectrometers a comprehensive summary of wood-hyperspectral imaging research is lacking. Near infrared hyperspectral imaging systems (NIR-HSI) typically have a wavelength range of 900-1700 nm, whereas short-wave infrared hyperspectral imaging (SWIR-HSI) systems range from 1000 to 2500 nm. We provide a detailed account of the various studies that have been published utilizing both camera types.

DOI： 10.1080/05704928.2022.2098759

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)  

Visible and near-infrared spectroscopy has been well studied for characterizing the organic compounds in fruit and vegetables from pre-harvest to late harvest. However, due to the challenge of decoupling of optical properties, the relationship between the collected samples’ spectral data and their properties, especially their mechanical properties (e.g., firmness, hardness, and resilience) is hard to understand. This study developed a time-resolved transmittance spectroscopic method to validate the light scattering changing characteristics in kiwifruit during shelf-life and in cold storage conditions. The experimental results demonstrated that the reduced scattering coefficient (μs′) of 846 nm inside kiwifruit decreased steadily during postharvest storage and is more evident under shelf-life than in cold storage conditions. Moreover, the correlation between the μs′ and the storage time was confirmed to be much higher than that using the external color indexes measured using a conventional colorimeter. Furthermore, employing time-resolved profiles at this single wavelength, an efficacious mathematical model has been successfully formulated to classify the stages of kiwifruit softening, specifically early, mid-, and late stages. Notably, classification accuracies of 84% and 78% were achieved for the shelf-life and cold storage conditions, respectively.

DOI： 10.1038/s41598-023-43777-5

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Walter de Gruyter GmbH  

Abstract

Conventional fluorescence spectroscopy has been suggested as a valuable tool for classifying wood species rapidly and non-destructively. However, because it is challenging to conduct absolute emission intensity measurements, fluorescence analysis statistics are difficult to obtain. In this study, another dimension of fluorescence, that is, fluorescence lifetime, was further evaluated to address this issue. A time-resolved fluorescence spectroscopic measurement system was first designed, mainly using a streak camera, picosecond pulsed laser at 403 nm, and a spectroscope, to collect the fluorescence time-delay (FTD) profiles and steady-state fluorescence intensity (FI) spectra simultaneously from 15 wood species. For data analysis, principal component analysis was used to “compress” the mean-centered FTD and FI spectra. Then, support vector machine classification analysis was utilized to train the wood species classification model based on their principal component scores. To avoid overfitting, ten-fold cross-validation was used to train the calibration model using 70 % of the total samples, and the remaining 30 % hold-out validation was used to test its reproducibility. The cross-validation accuracies were 100 % (5 softwoods) and 96 % (10 hardwoods), with test-validation accuracies of 96 % and 89 %.

DOI： 10.1515/hf-2023-0017

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Other Link： https://www.degruyter.com/document/doi/10.1515/hf-2023-0017/pdf

Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：ELSEVIER  

Nondestructive monitoring of the environmental changes around fluorescent molecules in postharvest fruit is highly informative for monitoring their ripening and early decay process. In this study, a time-resolved laser-induced fluorescence (TRLIF) spectroscopic system, including a streak camera, spectrometer, and 403 nm picosecond-pulsed laser, was first developed. Then, representative pigments in photosynthesis (chlorophyll a, chlorophyll b, and carotenoids liquid samples) were initially measured and studied to determine whether the developed TRLIF spectroscopic system could produce the characteristics of chlorophyll fluorescence decay traces. Finally, the same approach was tested to monitor the ripening and early decay processes of each three samples of avocado and strawberry. The overall findings demonstrated that the fluorescence lifetime (FLT) of the fruit samples declined steadily during the shelf-life storage. For the avocados samples, the average FLT loss at the wavelength range of 706-749 nm decreased by approximately 23 % and 31 % at 1 and 2 d under shelf-life storage, respectively. For the strawberry samples, the average FLT loss at the wavelength range of 631-706 nm decreased by approximately 22 %, 46 %, 68 %, and 73 % at 0.5, 1, 1.5, and 2 d under the same shelf-life storage condition. This study indicates that the developed TRLIF spectroscopic method is potentially robust and accurate for assessing the ripening and early decay process of various climacteric and non-climacteric fruit.

DOI： 10.1016/j.postharvbio.2022.112231

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In this study, an approach to visualize the spatial distribution of sugar content in white strawberry fruit flesh using near-infrared hyperspectral imaging (NIR-HSI; 913–2166 nm) is developed. NIR-HSI data collected from 180 samples of “Tochigi iW1 go” white strawberries are investigated. In order to recognize the pixels corresponding to the flesh and achene on the surface of the strawberries, principal component analysis (PCA) and image processing are conducted after smoothing and standard normal variate (SNV) pretreatment of the data. Explanatory partial least squares regression (PLSR) analysis is performed to develop an appropriate model to predict Brix reference values. The PLSR model constructed from the raw spectra extracted from the flesh region of interest yields high prediction accuracy with an RMSEP and (Formula presented.) values of 0.576 and 0.841, respectively, and with a relatively low number of PLS factors. The Brix heatmap images and violin plots for each sample exhibit characteristics feature of sugar content distribution in the flesh of the strawberries. These findings offer insights into the feasibility of designing a noncontact system to monitor the quality of white strawberries.

DOI： 10.3390/foods12050931

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：WALTER DE GRUYTER GMBH  

Substantial efforts have been undertaken for visualizing and simulating the characteristics of water movement in wood. However, three-dimensional (3D) thermo-hygric simulations still pose challenges, not only because wood has a heterogeneous microstructure that results in complex computational models but also because there is a lack of proper experimental techniques to support and validate the model constructions. In this study, the moisture distribution in wood during the water adsorption and desorption processes was first visualized using a near-infrared hyperspectral imaging (NIR-HSI) method that has high resolution, sensibility, and stability. Then, based on the moisture visualization results, the main parameters of a mass transfer simulation code were varied. The visualization and simulation results were confirmed to match well with the main characteristics; e.g., drying speed was slower in the wood parts with higher densities. Additionally, there was a relatively large gradient over the surface layer of the wood samples as the drying progressed, whereas this was not an obvious feature in the water adsorption process. Hence, this study proposes that the NIR-HSI method can be combined with thermo-hygric and 3D simulation model construction. Such an approach provides the basis for optimizing drying conditions and providing high-quality wood products.

DOI： 10.1515/hf-2021-0203

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：ELSEVIER  

Rapid and non-destructive detection of genuine Thai Jasmine rice (Khao Dawk Mali 105 (KDML105)) from Pathum Thani1 (PTT1) and Phitsanulok2 (PSL2) under either milled or brown conditions is required to disrupt fraudulent. This study aimed to resolve this real issue using long-wave near infrared hyperspectral imaging (NIRHSI) coupled with machine learning and deep learning approaches. The best classification accuracy for the milled rice was achieved using the spectral imaging-based analysis on the NIR-HSI data with selected wavelength, approximately 95% for the test set either by convolutional neural network or support vector machine (SVM), whereas for the brown rice, the SVM model based on the averaged NIR spectra could achieve the best classification accuracy of 95.4%. It suggests the chemical component difference and its spatial distribution in the milled rice could contribute higher classification accuracy. Additionally, the surface bran effects of brown rice could be reduced by using averaged spectral data coupled with the SVM method.

DOI： 10.1016/j.infrared.2022.104100

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.3390/foundations2030035

Language：English   Publishing type：Research paper (scientific journal)   Publisher：MDPI  

Abaca fibers were subjected to a TEMPO mediated oxidation to extract nanocellulose on a 500 L capacity locally fabricated reactor. A yield of 46.7% white gel material with 2.23% solid content was obtained from an overnight reaction. Transmission electron microscopy scan of the white gel material confirms the production of relatively short highly individualized cellulose nanofibril (CNF) as the diameter of abaca fiber was reduced from 16.28 mu m to 3.12 nm with fiber length in the range of 100 nm to 200 nm. Nanocellulose film was prepared using air drying (CNF-VC) and vacuum oven drying (CNF-OD). The effect of CNF concentration on the physical, morphological, thermal and mechanical properties were evaluated. FTIR spectra showed cellulose I spectra between abaca fiber with both the CNF-VC film and CNF-OD film with two distinct peaks at 1620 cm(-1) and 1720 cm(-1) attributed to the carboxyl group resulting from the TEMPO oxidation. In addition, the carboxyl group decreases in thermal stability of cellulose. Moreover, the XRD scan showed a decrease in crystallinity index of CNF films compared to abaca fibers. CNF-VC film showed the highest tensile strength at 0.4% concentration with 88.30 MPa, while a 89.72 MPa was observed for CNF-OD film at 0.8% concentration.

DOI： 10.3390/cryst12050601

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Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：ELSEVIER  

This paper reports an evaluation of firmness, soluble solids content (SSC), and acidity (pH) in kiwifruit using a newly designed visible-near-infrared (Vis-NIR) spatially resolved spectroscopic (SRS) system. The system mainly comprises a cost-effective Vis-NIR hyperspectral imaging camera, a halogen light source, and 36 light-receiving silica fibers which were divided into six groups (1, 2, 3, 4, 5, and 10 mm away from the light illumination) used to collect diffusely reflected light from sample surface. During the experiment, time-resolved spectroscopy (TRS) was used to validate the light scattering characteristics at a single wavelength of 846 nm by transmission measurement, which differed from the reflectance measurement of the SRS system. The TRS results show that firmer kiwifruits tended to have a lower transmitted light intensity and a higher full width at half maximum value. The SRS results indicate that the reflected light intensity decreased more with an increased distance from the illumination spot in firmer kiwifruits. The results of the two methods supported the same view, i.e., firmer kiwifruit indicated higher degrees of light scattering inside. Following on, the calibration models for kiwifruit properties were constructed using the SRS data coupled with partial least squares regression analysis. Finally, the prediction accuracies were benchmarked against standard diffuse reflectance spectroscopy using one fiber group position of the same SRS system. The overall results showed the benefits of using the SRS system to predict fruit firmness by enhancing light scattering effects and predicting the SSC required for reducing such effects

DOI： 10.1016/j.postharvbio.2022.111841

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：SPRINGERNATURE  

Depending on the uniformity of the quality attribute within agricultural products, there is often a need to develop non-destructive and efficient evaluation methods to assure their qualities. Near-infrared spectroscopy (NIRS) is a well-suited method to characterize organic compounds, particularly when coupled with multivariate analysis methods. This review article introduces scientific and technical reports using the NIRS to evaluate food, agriculture, and forest products. Overall, basic spectroscopic research is continuously progressing; indeed, in combination with big-data information technology and spectral imaging techniques, material analysis is improving to maximize performance. Portable and low-cost devices have also been designed and produced, enabling remote analysis. Future advancements are expected to result in its applications in even more fields for online or at-line quality monitoring.

DOI： 10.1007/s44211-022-00106-6

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Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：SPRINGER  

The complexities of wood microstructure cause difficulties in understanding water movement characteristics during drying. Here, the water transport dynamics in softwood (Japanese cypress) with different sample lengths (30 mm, 60 mm, and 90 mm) and various drying temperatures (30 degrees C, 60 degrees C, and 90 degrees C) were studied using a rapid and high-resolution moisture content (MC) mapping method based on long-wave near-infrared hyperspectral imaging (NIR-HSI). The observations of this study are as follows: slow drying at approximately 30 degrees C, the area near the subsurface of the wood samples tends to have higher MC than the central parts during drying, especially in the case of longer wood samples. For drying at higher temperatures, strongly bonded water appeared at the surface areas much earlier, which could easily cause sample deformation and cracking. Overall, the experimental results suggest the capillary effects could play a major role at the first stage of slow drying at fiber level; then, the transfers between bound and free water could play a significant power source in the second drying stage. It is expected that this study will be of help in providing a basis to study and simulate the drying characteristics of cellular and hydrophilic materials.

DOI： 10.1007/s10570-021-04290-y

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：WALTER DE GRUYTER GMBH  

Acacia, including Acacia hybrids, are some of the most important species grown as part of the Viet-namese wood industry. Rapid methods to identify the variations in the wood properties of Acacia hybrids how -ever, are a currently lacking and creating limits for their breeding programs. In this study, nine Acacia hybrid clones, including those that were diploid, triploid, and tetraploid were evaluated using near-infrared spectros-copy (NIR) and hyperspectral imaging (HSI). The standard normal variate (SNV) and second derivative (SP2D) were applied to compare the performances of NIR and HSI using partial least square regression. The HSI images were ac-quired at wavelengths from 1033 to 2230 nm and the SNV and SP2D described the variations in the wood properties. The NIR predicted the wood physical properties better than HSI, while they provided similar predictions for the mechanical properties. The mapping results showed low densities around the pith area and high densities near the bark. They also revealed that the air-dry moisture content changed at different positions within a disk and was dependent on its position within the tree. Overall, NIR and HSI were found to be potential wood property prediction tools, suitable for use in tree improvement programs.

DOI： 10.1515/hf-2021-0024

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

DOI： 10.1007/s10570-021-04239-1

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Other Link： https://link.springer.com/article/10.1007/s10570-021-04239-1/fulltext.html

Language：English   Publishing type：Research paper (scientific journal)   Publisher：MDPI AG  

The current mechanical harvesting methods of Lycium barbarum L. are labor intensive and cause too much damage, but vibrating and comb-brushing harvesting can increase the efficiency while minimizing the damage. However, optimizing the main factors and their parameter values of vibrating and comb-brushing harvesting is challenging. To achieve the high-efficiency and low-damage harvesting of L. barbarum, firstly, the mechanical models of the materials used in the experiments were established based on the physical tests. Then, the vibrating and comb-brushing harvesting simulations were conducted based on FEM to acquire the ranges of the parameter values. The effects of the rotating speed, material, and amplitude on the harvesting rate of ripe fruit and harvesting rate of unripe fruit, as well as the damage rate of ripe fruit were determined based on RSM. Finally, the optimized parameters were obtained and verified using the field experiments. The field experiments showed that the harvesting rate of ripe fruit was 85.8%, the harvesting rate of unripe fruit was 10.5%, and the damage rate of ripe fruit was 9.7%. The findings provided the optimal parameter values, which were a design basis for the vibrating and comb-brushing harvesters of L. barbarum.

DOI： 10.3390/horticulturae7090286

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：MDPI AG  

The most common harvesting method of Lycium barbarum L. (L. barbarum) is manual harvesting, resulting in low efficiency and high cost. Meanwhile, the efficiency of vibration harvesting, which is considered an efficient mechanical harvesting method, can be significantly improved if the optimized resonance frequency of the shrub can be obtained. To vibration harvest fruit efficiently, a 3D model of the shrub was established based on measurements of the shape parameters, and material mechanics models of the branches were established based on physical tests. The modal analysis of the shrub based on finite element method (FEM) simulation was performed to obtain the range of resonance frequency, and the modal experiment of the shrub using acceleration sensors and an impact hammer was conducted to obtain the accurate resonance frequency. Based on the results of the modal analysis and experiment, the optimized resonance frequency was determined to be 2 Hz. The field experiment showed that the fruit fell off when the branches were vibrated at this frequency. The results provide the design basis for the efficient vibration harvesting of L. barbarum.

DOI： 10.3390/agriculture11060519

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：MDPI AG  

When harvesting Lycium barbarum L., excess amounts of detachments of the half-ripe fruit, unripe fruit, flowers, and leaves significantly affect the yield and adversely affect the subsequent processing, such as drying and grading. Finite element method (FEM) simulations and experiments of detachments were performed to harvest more ripe fruit and less half-ripe fruit, unripe fruit, flowers, and leaves. Three-dimensional (3D) models of the ripe fruit, half-ripe fruit, unripe fruit, flowers, leaves, fruit calyxes (flower calyx), fruit stems (flower stem), and branches were constructed using a 3D scanner, and material mechanics models of the above parts were established based on physical tests with universal testing machines. Detachment simulations and experiments of the ripe fruit, half-ripe fruit, unripe fruit, flowers, and leaves were performed to determine the detachment mechanisms and sequences. The detachment forces of each set of two parts were obtained. The field experiments showed that the detachment force between the fruit and calyx of ripe fruit was the lowest value of these forces, and only the ripe fruit was the first to detach from the calyx when harvesting. The results provided data support on the mechanics properties of wood and the optimization basis for the harvesting method of L. barbarum.

DOI： 10.3390/f12060699

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)  

© 2020 Walter de Gruyter GmbH, Berlin/Boston 2020. Although visible and near-infrared (Vis-NIR) spectroscopy can rapidly and nondestructively identify wood species, the conventional spectrometer approach relies on the aggregate light absorption due to the chemical composition of wood and light scattering originating from the physical structure of wood. Hence, much of the work in this area is still limited to further spectral pretreatments, such as baseline correction and standard normal variate to reduce the light scattering effects. However, it should be emphasized that the light scattering rather than absorption in wood is dominant, and this must be effectively utilized to achieve highly accurate and robust wood classification. Here a novel method based on spatially resolved diffuse reflectance (wavelength range: 600-1000 nm) was demonstrated to classify 15 kinds of wood. A portable Vis-NIR spectral measurement system was designed according to previous simulations and experimental results. To simplify spectral data analysis (i.e., against overfitting), support vector machine (SVM) model was constructed for wood sample classification using principal component analysis (PCA) scores. The classification accuracies of 98.6% for five-fold cross-validation and 91.2% for test set validation were achieved. This study offers enhanced classification accuracy and robustness over other conventional nondestructive approaches for such various kinds of wood and sheds light on utilizing visible and short-wave NIR light scattering for wood classification.

DOI： 10.1515/hf-2020-0074

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)  

© 2020 Elsevier B.V. This work aimed to offer a non-destructive and fast approach to visualizing the soluble solids content (SSC) and acidity (pH) of the whole kiwifruit. Most of the visible-near-infrared spectral imaging techniques used in postharvest fruit and vegetables assessment exhibit issues related to the identification of the quality spatial distribution within intact samples, mainly due to sampling surface curvature effects. Here, a push-broom-type NIR hyperspectral imaging camera and a sample rotation stage were combined to scan entire kiwifruit surfaces. Then, key wavelengths in the range of 1002–2300 nm were extracted for constructing SSC and pH calibration models by partial least squares regression analysis. The resulting SSC prediction accuracy was sufficiently high: the coefficient of determination (R2cv) and the root mean square error (RMSEcv) of cross-validation set were 0.74 and 0.7 %, respectively. For pH, the R2cv and RMSEcv were 0.64 and 0.14, respectively. Finally, the SSC and pH 360˚mapping results surpassed earlier works in this area that they showed a distinct spatial distribution within each intact sample. It was concluded that the proposed object rotation hyperspectral imaging approach is promising for the non-destructive prediction mapping of SSC and pH in kiwifruit or other cylindrical-shaped samples.

DOI： 10.1016/j.postharvbio.2020.111440

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)  

© 2020 Walter de Gruyter GmbH, Berlin/Boston 2020. The objective of this work was to provide a rapid and nondestructive imaging method for evaluating the hygroscopic behavior of thermally modified lignocellulosic materials (softwood and hardwood). The difference in the hygroscopic behavior was explained by moisture content (MC) mapping results and molecular association characteristics of absorbed water (i.e. weakly, moderately, and strongly hydrogen-bonded water molecules) with wood at various relative humidities (RH). To achieve this goal, near-infrared (NIR) spectral images in the wavelength range 1816-2130 nm (covering the combination of stretching and deformation vibrations for OH) were used to visualize MC distributions over the surface of Japanese cedar and European beech samples which had been thermally treated at different temperatures. A curve fitting method was utilized to explore changes in water-wood structure characteristics based on shifts to longer wavelength in spectral signals caused by increasing MC. The curve fitting results support the recent nuclear magnetic resonance (NMR) studies that different bound water stabilities may pool in different compartments of the wood cell wall. Furthermore, water was firmly bound to wood at low RHs and H-bonds gained mobility as the number of absorbed molecules increased. It is concluded that NIR hyperspectral imaging also has the potential to be a complementary methodology for studying the transient changes of wood-water interactions before equilibrium.

DOI： 10.1515/hf-2019-0298

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)  

© 2020 Elsevier B.V. Visible–near infrared (Vis–NIR) spectroscopy is a rapid and nondestructive method used to characterize organic compounds in postharvest fruit and vegetable assessment. However, developing robust calibration models is a challenge as conventional spectrometers collect only the cumulative effects of light absorption and scattering. In this study, a multifiber-based Vis–NIR spatially resolved spectra measurement system was designed for simultaneous evaluation of soluble solid content (SSC) and firmness in apple. Thirty silica fibers separated into five groups at 1, 2, 3, 4, and 5 mm away from the light illumination point and connected to a cost-effective Vis–NIR hyperspectral imaging camera were used to acquire spectral data with an improved signal-to-noise ratio (S/N) by a two-step signal averaging process (i.e., 30 camera pixels per fiber and six optical fibers per group). Reflectance ratio spectra were then calculated by dividing the diffusely reflected light intensity detected at distance d +△ by that detected at distance d to realize a light reference-free approach. Finally, the useful explanatory variables were selected by competitive adaptive reweighted sampling (CARS) to construct individual calibration models for various regions. The coefficients of determination (Rcal2) and the root mean square errors (RMSEcal) of the best-performing calibration models were approximately 0.97 and 0.20 % for SSC and 0.96 and 0.37 N for firmness, respectively. Furthermore, the predicted results were 0.92 and 0.35 % for SSC and 0.87 and 0.71 N for firmness. Our method offers low-cost and portable detection of SSC and firmness for postharvest fruit evaluation.

DOI： 10.1016/j.postharvbio.2020.111417

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/jpostharvbio.2020.111417

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Authorship：Lead author   Language：English   Publishing type：Part of collection (book)  

Near-infrared spectroscopy (NIRS) is suitable for both the qualification and quantification of organic properties associated with C-H, O-H, or N-H groups. There have been considerable efforts made toward proposing and developing various technologies and devices for the rapid and nondestructive measurement of various samples related to natural materials and environmental sciences. In this chapter, the utilizations of NIRS in the fields of wood material, soil, sediment, waste liquid, atmospheric gas detection, and archeological science will be explained through some representative studies.

DOI： 10.1007/978-981-15-8648-4_16

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)  

© 2020 Elsevier B.V. Seeds are the basis of the agricultural food industry, greater insights into seed viability before sowing could improve storage management and field performance. In the present study, we aimed to address this issue by using highly cost-efficient near-infrared hyperspectral imaging (NIR-HSI) and a convolutional neural network (CNN) deep learning approach. An NIR-HSI camera was used because it can recognize both molecular vibration information (i.e. chemical component differences) and its spatial distribution in each seed sample; this camera is much more informative than a regular RGB digital camera. Using this technology, the emphasis of this study was firstly to provide a methodology for enhancing the interpretability of viable and non-viable seeds via principal component analysis (PCA) and support vector machine (SVM) viability classification analysis of NIR-HSI data. A CNN was then constructed to“cognize” the differences in viable and non-inviable seeds and classify them automatically. Experimental results indicate that the methodology produces a ~90% classification accuracy for both a five-fold cross-validation set and a test set of naturally aged Japanese mustard spinach seeds. Therefore, this study provides a new strategy for effective and practical seed viability prediction.

DOI： 10.1016/j.compag.2020.105683

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： https://doi.org/10.1515/hf-2019-0298

Language：English   Publishing type：Research paper (scientific journal)  

© 2020 by the authors. Acacia plants are globally important resources in the wood industry, but particularly in Southeast Asian countries. In the present study, we compared the physical and mechanical properties of polyploid Acacia (3x and 4x) clones with those of diploid (2x) clones grown in Vietnam. We randomly selected 29 trees aged 3.8 years from different taxa for investigation. BV10 and BV16 clones represented the diploid controls; X101 and X102 were the triploid clones; and AA-4x, AM-4x, and AH-4x represented neo-tetraploid families of Acacia auriculiformis, Acacia mangium, and their hybrid clones. The following metrics were measured in each plant: stem height levels, basic density, air-dry equilibrium moisture content, modulus of rupture (MOR), modulus of elasticity (MOE), compression strength, and Young's modulus. We found that the equilibrium moisture content significantly differed among clones, and basic density varied from pith-to-bark and in an axial direction. In addition, the basic density of AA-4x was significantly higher than that of the control clones. Furthermore, the MOR of AM-4x was considerably lower than the control clones, whereas the MOE of X101 was significantly higher than the control values. The compression strength of AM-4x was significantly lower than that of the control clones, but AH-4x had a significantly higher Young's modulus. Our results suggest that polyploid Acacia hybrids have the potential to be alternative species for providing wood with improved properties to the forestry sector of Vietnam. Furthermore, the significant differences among the clones indicate that opportunities exist for selection and the improvement of wood quality via selective breeding for specific properties.

DOI： 10.3390/f11070717

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1007/s10570-020-03117-6

Language：English   Publishing type：Research paper (scientific journal)  

© 2019 The Royal Society of Chemistry. From the viewpoint of combating illegal logging and examining wood properties, there is a contemporary demand for a wood species identification system. Several nondestructive automatic identification systems have been developed, but there is room for improvement to construct a highly reliable model. The present study proposes cognitive spectroscopy that combines near infrared hyperspectral imaging (NIR-HSI) with a deep convolutional neural network approach. We defined "cognitive spectroscopy" as a protocol that extracts features from complex spectroscopic data and presents the best results without human intervention. Overall, 120 samples representing 38 hardwood species were scanned using an NIR-HSI camera. A deep learning prediction model was built based on the principal component (PC) images obtained from the PC scores of hyperspectral images (wavelength range: 1000-2200 nm at approximately 6.2 nm interval). The results showed that the accuracy of wood species identification based on 6PC (PC1-PC6) images was 90.5%, which was considerably higher than the accuracy of 56.0% obtained with conventional visible images.

DOI： 10.1039/c9an01180c

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PubMed

Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)  

© 2019 Walter de Gruyter GmbH, Berlin/Boston. As the strength of wood is greatly affected by its three-dimensional (3D) grain angles (the dive angle and the surface angle), the wood industry today requires automatic, rapid, and robust measurement techniques for measuring them simultaneously. In the present study, a near infrared spatially and spectrally resolved imaging (NIR-SSRI) system was designed in a line scan model, mainly including an NIR hyperspectral imaging camera and a halogen spotlight source (Ø 1 mm). Spatially resolved diffuse reflectance images at three target wavelengths (1002 nm, 1217 nm, and 1413 nm) were obtained from Hinoki cypress [Chamaecyparis obtusa (Siebold & Zucc.) Endl.] samples at various (0°, 3°, 6°, ⋯ 45°) dive angles and surface angles (0°, 3°, 6°, ⋯ 45°). The scattering patterns caused by the "tracheid effect" were almost elliptical. Subsequently, nonlinear least squares fitting was used to determine their eccentricities (e) and rotation angles (θ). The e values at each selected wavelength were highly correlated with the dive angle reference values; and the global identification model developed using Gaussian process regression (GPR) under five-fold cross-validation (CV) reached a determination coefficient (r2) of 0.98 with a root mean square error (RMSE) of 2.2°. On the other hand, local surface angle identification models developed using linear regression analysis achieved determination coefficients higher than 0.90 on r2 and an RMSE of CV lower than 3.8° when the dive angle was lower than 30°.

DOI： 10.1515/hf-2018-0273

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Language：Japanese   Publishing type：Research paper (scientific journal)  

DOI： 10.1177/0967033518808053

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1515/hf-2018-0060

Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Walter de Gruyter GmbH  

© 2018 Walter de Gruyter GmbH, Berlin/Boston. A near-infrared spatially resolved spectroscopy (NIR-SRS) system, also called the hyperspectral imaging system, was applied in a line scanning model combined with a concentrated halogen light source (Ø 1 mm), and spatially resolved reflectance images of Douglas fir [Pseudotsuga menziesii (Mirb.) Franco] wood were obtained. The samples with various densities, grain directions and thicknesses were observed in the NIR range of 1000-1600 nm. Then, a nonlinear curve-fitting algorithm was applied based on the steady-state diffusion theory model to estimate the absorption coefficient (μa), and reduced scattering coefficients (μ′s μ) both parallel and perpendicular to the grain direction at each measurement position. The absorption scattering coefficients at 1457 nm and two kinds of reduced scattering coefficients at 1002 nm were highly correlated with wood densities measured by an X-ray densitometer. The correlation coefficients were 0.953 and 0.987 for 3 mm and 5 mm samples, respectively, while three optical profiles were combined. It can be concluded that NIR-SRS is a fast and simple method for measuring the optical characteristics of softwood, although it has a non-homogeneous cellular structure. Sub-surface density and grain direction could be predicted with satisfactory accuracy based on a few key wavelengths without relying on multivariate statistical analysis.

DOI： 10.1515/hf-2017-0213

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier Ltd  

© 2017 Elsevier Ltd Near-infrared (NIR) hyperspectral imaging was used to evaluate soluble solids content (SSC) in ‘Fuji’ apples [Malus sylvestris (L.) Mill. var. domestica (Borkh. Mansf.)]. Eighty ‘Fuji’ apples were analyzed by collecting four small block samples from each one (approximately 2.0 cm × 2.0 cm × 1.5 cm). Partial least squares (PLS) regression analysis was performed to determine the relation between SSC reference data and NIR spectral data measured from each sample. The cross-validation coefficient of determination (r2) between predicted and measured SSC values is 0.89 with a root mean squared error of cross-validation (RMSECV) of 0.55%. Then, we successfully mapped SSC at a high spatial resolution (375 μm per pixel). In addition, the absorption and reduced scattering coefficients of the measured samples were determined based on a diffusion theory model. The absorption coefficients are positively correlated to the SSC values (chemical information), whereas water cored tissue content (physical information) causes a characteristic change in light scattering coefficients. The fitting results were validated by Monte Carlo simulation, and the light penetration depth in ‘Fuji’ apples was estimated to be around 0.33 cm at 1198 nm and 0.17 cm at 1450 nm, respectively.

DOI： 10.1016/j.jfoodeng.2017.12.028

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：SAGE Publications Ltd  

© 2018, © The Author(s) 2018. To construct robust calibrations of wood properties by near infrared spectroscopy, one must independently evaluate the spectral contributions of light absorption and light scattering. However, the light propagation in wooden cellular structures is difficult to interpret because these structures are complex, heterogeneous, and anisotropic. This study investigates the reduced scattering coefficients of softwood and hardwood (with ring-porous or diffuse-porous vessels) at 846 nm by time-resolved spectroscopy. It also evaluates the effect of wooden cellular structure and air-dry density on the light propagation. After determining the reduced scattering coefficients, we observed cross-sectional microscopic images of the wood samples. Eighty-five percent of the variation in the reduced scattering coefficients was explainable by the air-dry density, area ratio of the cell wall, and the median pore area. Monte Carlo simulations of the light propagation through wood revealed that most of the photon transport occurs in the cell-wall substance.

DOI： 10.1177/0967033518757233

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Language：Japanese   Publishing type：Research paper (scientific journal)  

DOI： 10.1080/10942912.2018.1476378

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1515/hf-2018-0128

Language：English   Publishing type：Research paper (scientific journal)   Publisher：FRONTIERS MEDIA SA  

© 2017 Yang, Inagaki, Ma and Tsuchikawa. Nitrate is an important component of the nitrogen cycle and is therefore present in all plants. However, excessive nitrogen fertilization results in a high nitrate content in vegetables, which is unhealthy for humans. Understanding the spatial distribution of nitrate in leaves is beneficial for improving nitrogen assimilation efficiency and reducing its content in vegetables. In this study, near-infrared (NIR) hyperspectral imaging was used for the non-destructive and effective evaluation of nitrate content in spinach (Spinacia oleracea L.) leaves. Leaf samples with different nitrate contents were collected under various fertilization conditions, and reference data were obtained using reflectometer apparatus RQflex 10. Partial least squares regression analysis revealed that there was a high correlation between the reference data and NIR spectra (r2 = 0.74, root mean squared error of cross-validation = 710.16 mg/kg). Furthermore, the nitrate content in spinach leaves was successfully mapped at a high spatial resolution, clearly displaying its distribution in the petiole, vein, and blade. Finally, the mapping results demonstrated dynamic changes in the nitrate content in intact leaf samples under different storage conditions, showing the value of this non-destructive tool for future analyses of the nitrate content in vegetables.

DOI： 10.3389/fpls.2017.01937

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PubMed

Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：WALTER DE GRUYTER GMBH  

© 2017 Walter de Gruyter GmbH, Berlin/Boston. Wood density and microfibril angle (MFA) are strongly correlated with wood stiffness, swelling/shrinkage, and its anisotropy. Understanding the spatial distribution of these data is critical for solid timber applications. In this study, near-infrared (NIR) hyperspectral imaging has been calibrated for evaluation of wood density and MFA in an effective manner. Briefly, five wood samples collected from both normal wood (NW) and compression wood (CW) moieties of two different Cryptomeria japonica trees were analyzed. Partial least squares (PLS) regression analysis was performed to determine the relationship between X-ray densitometry data obtained by SilviScan and NIR spectra, and cross-validation (leave-one-out) approach served for prediction performances. The validation coefficient of determination (r2) between the predicted densities by the NIR technique and the X-ray data was 0.83 with a root mean squared error of cross-validation (RMSECV) of 105.2 kg m-3. Regarding MFA, the r2 was 0.77 and RMSECV 5.36°. Wood density was successfully maped as well as the MFA at a high spatial resolution. As a result, the detection of annual growth ring features and evaluation of aspects of heterogeneous wood quality has been facilitated. The mapping results were visually checked by looking at the difference between earlywood (EW) and latewood (LW) for density and by means of the Mäule color reaction indicating high lignin contents in CW in terms of MFA validation as CWs have high MFA values.

DOI： 10.1515/hf-2016-0153

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (scientific journal)  

Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)  

Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：N I R PUBLICATIONS  

© IM Publications LLP 2016. Foreign matters in food products strongly affect the commercial viability of food production companies. To monitor and improve the reliability of food production processes, fast and non-destructive online inspection methods are urgently required. In this study, we exploited the high transmittance of near infrared (NIR) light, and designed an NIR transmission-type imaging device that detects lowdensity foreign matters (such as insects) inside chocolate. Insect parts had consistently lower transmittance than chocolate parts. To enhance the important differences between the two parts, the images were processed using principal component analysis. Furthermore, an algorithm for detecting the insect parts was developed in conjunction with image threshold processing and Laplacian of Gaussian edge detection. A favourable detection rate (93%) was achieved using spectral regions 840-870 nm, 870-900 nm and 900-930 nm. The results suggested that NIR multispectral imaging is useful for detecting low-density matter (insects) in the chocolate manufacturing industry.

DOI： 10.1255/jnirs.1237

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