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    Near-Infrared Hyperspectral Imaging to Predict Intact Sweet Tamarind Fruit Quality
    (2026-07-01)
    Sahachairungrueng, Woranitta
    ;
    Aozora, Wayan Dipasasri
    ;
    Tantinantrakun, Achiraya
    ;
    Suwapanich, Rachit
    ;
    Workhwa, Saranya
    The quality of sweet tamarind fruit, as determined by its total soluble solids (TSS), titratable acidity (TA), and TSS/TA ratio, is important for consumer satisfaction. Nondestructive techniques are therefore required to assess the quality of sweet tamarind fruit. This study investigated whether near-infrared hyperspectral imaging (NIR-HSI) in the wavelength range of 935–1720 nm can be used as a non-destructive method to assess TSS, TA, and the TSS/TA ratio of sweet tamarind fruit and to classify it under commercial standards. NIR-HSI-based chemometric and machine-learning modeling was applied for quantification and qualification analyses. Calibration models for determining TSS, TA, and the TSS/TA ratio were developed using partial least squares regression (PLSR) and support vector machine regression (SVMR). A combination of first derivative and SNV spectral pretreatment was optimized to establish an SVMR model for TSS determination. MSC spectral pretreatment was optimized to develop the SVMR model for TA assessment, and the first derivative spectral pretreatment was optimized to establish an SVMR model for the TSS/TA ratio. Correlation coefficients of prediction (R<inf>p</inf>) of 0.959, 0.961 and 0.957 were obtained with root mean square errors of prediction (RMSEP) of 1.102%, 0.369% and 7.850, and a ratio of performance to deviation (RPD) of 3.29, 3.52 and 3.34 for the TSS, TA, and TSS/TA ratio evaluations, respectively. Partial least squares–discriminant analysis (PLS-DA) and support vector machine classification (SVMC) were used for classifying sweet tamarind fruit under a commercial acidity standard (≤4%). The SVMC with SNV spectral pretreatment produced the best prediction results for distinguishing standard and off-standard sweet tamarind fruit with an 82.86% accuracy. NIR HSI can be used to non-destructively predict the quality of tamarind fruit. It can be applied for online sorting to evaluate individual sweet tamarind fruits for grading and quality control in factory environments.
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    Detection of Hardening in Mangosteens Using near-Infrared Hyperspectral Imaging
    (2024-04-01)
    Workhwa, Saranya
    ;
    Khanthong, Thitirat
    ;
    Manmak, Napatsorn
    ;
    Thompson, Anthony Keith
    ;
    Teerachaichayut, Sontisuk
    Mangosteens can develop a postharvest physiological disorder, called “hardening”, which affects their marketability and is not detectable using visual inspection. The hardening disorder of mangosteens was determined by firmness value using the texture analyzer. Near-infrared hyperspectral imaging (NIR-HSI) in the region of 935–1720 nm was tested as a possible rapid and non-destructive method to detect this disorder. The spectra from a region of interest of mangosteens were acquired and used for analysis. Calibration models for firmness of a similarly sized group and a mixed-size group were established using partial least squares regression (PLSR) and support vector machine regression (SVMR). Chemometric algorithms were investigated in order to determine the optimal conditions for establishing the models for firmness. The optimum model was obtained when the fruit were graded into similarly sized groups. Using partial least squares regression (PLSR), the correlation coefficient of prediction (R<inf>p</inf>) was 0.87 and the root mean square error of prediction (RMSEP) was 6.25 N. The predictive images for firmness of the fruit were created by interpreting predicted firmness visualized as colors in every pixel. From the data, it was concluded that NIR-HSI can potentially be used to visualize hardening of individual mangosteens based on their predictive images.
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    Assessment of Nitrite Content in Vienna Chicken Sausages Using Near-Infrared Hyperspectral Imaging
    (2023-07-01)
    Tantinantrakun, Achiraya
    ;
    Thompson, Anthony Keith
    ;
    Terdwongworakul, Anupun
    ;
    Teerachaichayut, Sontisuk
    Sodium nitrite is a food additive commonly used in sausages, but legally, the unsafe levels of nitrite in sausage should be less than 80 mg/kg, since higher levels can be harmful to consumers. Consumers must rely on processors to conform to these levels. Therefore, the determination of nitrite content in chicken sausages using near infrared hyperspectral imaging (NIR-HSI) was investigated. A total of 140 chicken sausage samples were produced by adding sodium nitrite in various levels. The samples were divided into a calibration set (n = 94) and a prediction set (n = 46). Quantitative analysis, to detect nitrate in the sausages, and qualitative analysis, to classify nitrite levels, were undertaken in order to evaluate whether individual sausages had safe levels or non-safe levels of nitrite. NIR-HSI was preprocessed to obtain the optimum conditions for establishing the models. The results showed that the model from the partial least squares regression (PLSR) gave the most reliable performance, with a coefficient of determination of prediction (R<inf>p</inf>) of 0.92 and a root mean square error of prediction (RMSEP) of 15.603 mg/kg. The results of the classification using the partial least square-discriminant analysis (PLS-DA) showed a satisfied accuracy for prediction of 91.30%. It was therefore concluded that they were sufficiently accurate for screening and that NIR-HSI has the potential to be used for the fast, accurate and reliable assessment of nitrite content in chicken sausages.
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    Nondestructive quality assessment of longans using near infrared hyperspectral imaging
    (2022-03-30)
    Sahachairungrueng, Woranitta
    ;
    Teerachaichayut, Sontisuk
    Near infrared hyperspectral imaging (NIR-HSI) is a method that can be used to evaluate the quality of fruit nondestructively. The objective of this research was to study the feasibility of NIR-HSI reflectance mode, within the wavelength of 935-1720 nm, for predicting the quality of longans. The two important factors chosen were: total soluble solids (TSS) and moisture content (MC). Each longan was assessed by first measuring its spectral data then measuring its TSS and MC to establish calibration models using multiple linear regression (MLR) compared with partial least squares regression (PLSR). Original spectra of longans gave the optimum results by PLSR for developing the models with correlation coefficients (Rp) of 0.76 for TSS and 0.88 for MC as well as root mean square error of predictions (RMSEP) of 0.42% and 0.45% respectively. By image processing, the predictive images from the models for TSS and MC were created based on color scales. They showed different colors of longans related to the level of TSS and MC and the deviation in levels in different parts of each longan by the predictive image. The results showed it could be used for grading fruit giving NIR-HSI potential to be developed in on-line systems.
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    Quantification of acidity and total soluble solids in guavas by near infrared hyperspectral imaging
    (2018-11-09)
    Klinbumrung, Nutsinee
    ;
    Teerachaichayut, Sontisuk
    In order to provide premium quality for marketing of guavas the titratable acidity (TA) and total soluble solids (TSS) levels should be determined. A reflectance near infrared hyperspectral imaging (NIR-HSI) unit in the wavelength range of 936-1696 nm, which is a nondestructive technique, was tested for use in predicting TA and TSS. Samples of 100 guavas were scanned by NIR-HIS as a group for calibration (N=67) and as a group for prediction (N=33). The average spectra from the region of interest (ROI) of samples were used to establish the calibration models for TA and TSS by using partial least squares regression (PLSR) to establish calibration models. The calibration model for TA gave a coefficient of determination (R<sup>2</sup>) of 0.972 and the root mean square error of prediction (RMSEP) of 0.010% and for TSS the R<sup>2</sup> was 0.801 and the RMSEP was 0.437°Bx. The accuracies of these results indicate that NIR-HSI has potential for use in measuring TA and TSS of guavas.