Teerachaichayut, Sontisuk

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Teerachaichayut, Sontisuk
Alternative Name
Teerachaichayut, S.
Main Affiliation
Email
sontisuk.te@kmitl.ac.th

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    Classification of sweet corn based on storage time after harvest using near infrared spectroscopy
    (2017-03-21)
    Suktanarak, S.
    ;
    Supprung, P.
    ;
    Teerachaichayut, S.
    The freshness of sweet corns is important for production of canned sweet corn. The quality of sweet corns changes rapidly after harvest. Sweet corns should be processed through a production line as fast as possible after harvest. Therefore, some methods of classification of sweet corns based on storage time after harvest are needed. In this study, near infrared (NIR) spectroscopy operating in reflectance mode (1000-2500 nm) and interactance mode (588-1091) were investigated as methods of classification. Sweet corns both with and without husk were tested. Samples (n=120) were scanned with a NIR spectrophotometer every 6 h after harvest. They were then classified into two groups (0 and 1) with a 24-h after harvest cut-off time between the two groups (<24 h and ≥24 h). Classification models were established and validated with a calibration set (n=80), and then the accuracies of the models were evaluated with a prediction set (n=40), using a partial least squares discriminant analysis (PLSDA). It was found that second derivative spectral pretreatment gave the best results for NIR operating in reflectance mode. Regarding prediction accuracy, showed the best accuracies for both unhusked and husked sweet corns (100%), while it was found that mean center and second derivative spectral pretreatment gave good results for NIR operating in interactance mode. The predictive accuracies for unhusked and husked sweet corns obtained 90 and 97.5%, respectively. All of the results demonstrated that NIR spectroscopy has a real potential for non-destructive classification of sweet corns based on storage time after harvest.
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    Rapid detection of potassium sorbate in coconut water using near infrared hyperspectral imaging
    (2026-01-01)
    Tantinantrakun, Achiraya
    ;
    Kumpa, Benjaporn
    ;
    Ainkast, Pranpriya
    ;
    Thompson, Anthony Keith
    ;
    Teerachaichayut, Sontisuk
    Potassium sorbate may be illegally added to fresh coconut water in order to prolong its marketable life, but this adulteration may not be identified on the product label. The aim of this research was therefore to evaluate if samples of fresh coconut water that had been adulterated with measured amounts of potassium sorbate could be detected by near infrared hyperspectral imaging (NIR-HSI). Samples of coconut water with different potassium sorbate concentrations (N = 100) and pure coconut water samples (N = 100) were used in this study with their averaged spectral data used as independent variables. The smoothing spectral pretreatment gave the highest classification accuracy of 98.48% by partial least squares discriminant analysis (PLS-DA). While support vector machine regression (SVMR) with spectral pretreatment, using the 1st derivative combined with multiplicative scatter correction (MSC), achieved the optimum condition for developing the calibration model for determining potassium sorbate concentration with the correlation coefficient of prediction (R<inf>p</inf>) of 0.818 and the root mean square error of prediction (RMSEP) of 327.86 ppm. The results showed that NIR-HSI was able to be used as a fast, reliable, economic and environmentally friendly method of detecting potassium sorbate addition to coconut water.
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    Detection of adulteration of tapioca starch with dolomite by near infrared hyperspectral imaging
    (2020-01-01)
    Khamsopha, Duangkamolrat
    ;
    Teerachaichayut, Sontisuk
    Tapioca starch adulterated with dolomite is sold in markets, but this adulteration cannot be identified by normal visual inspection. Near infrared (NIR) hyperspectral imaging has been successfully used as a non-destructive method of identifying various characteristics of food, therefore it was tested to identify dolomite adulteration. Adulterated tapioca starch samples were prepared by adding dolomite in the range of 0.5-100% (wt/wt). Samples (N=400) of pure tapioca starch (0) and adulterated tapioca starch (1) were divided into calibration set (N=300) and a prediction set (N=100). All samples were scanned using NIR hyperspectral imaging (935-1720 nm) and spectra were pre-processed using Savitzky-Golay first derivative differentiation pretreatment in order to obtain the optimal conditions for establishing a classification model. Partial least squares-discriminant analysis was carried out to evaluate the accuracy of classification tapioca starch adulterated with dolomite. The results showed the total accuracy of prediction for classification was 100%. Therefore, NIR hyperspectral imaging was demonstrated to have a potential for use in detecting adulteration of tapioca starch with dolomite.