Teerachaichayut, Sontisuk

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

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    Item type:Publication,
    Non-destructive prediction of total soluble solids, titratable acidity and maturity index of limes by near infrared hyperspectral imaging
    (2017-11-01)
    Teerachaichayut, Sontisuk
    ;
    Ho, Huong Thanh
    This study was implemented for non-destructive prediction of total soluble solids (TSS), titratable acidity (TA) and calculation of TSS/TA as a measure of maturity index in intact limes using laboratory-based push-broom hyperspectral imaging (HSI) in reflectance mode in the range of 929–1671 nm. Limes were scanned by the HSI system in order to develop calibration models for predicting TSS, TA and TSS/TA using partial least square regression (PLSR). Original spectra obtained optimal conditions for establishing the models for TSS and TA while smoothing spectra for TSS/TA. The accuracy of the models for TSS, TA and TSS/TA provided coefficient of determination of prediction (R<sup>2</sup><inf>p</inf>) of 0.838, 0.694 and 0.775, respectively and root mean square errors of prediction (RMSEP) of 0.237%, 0.288% and 0.049, respectively. Image processing algorithms were then built up by interpreting predictive values, from the models, to colors in each pixel of the images. The predictive visualization of TSS, TA and TSS/TA in all portions of the limes based on a color scale was presented. The results showed that the HSI technique has the capability of predicting TSS, TA and TSS/TA of intact limes non-destructively and the results could be visualized by different colors of the predictive images.
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    Quantitative prediction of nitrate level in intact pineapple using Vis-NIRS
    (2015-01-01)
    Srivichien, Sasathorn
    ;
    Terdwongworakul, Anupun
    ;
    Teerachaichayut, Sontisuk
    Before pineapples are canned, the ones with high nitrate level must be sorted out first because nitrate causes black stains on the surface of the can; therefore, a nondestructive technique for sorting out pineapples is clearly needed. The use of visible and near infrared (Vis-NIR) spectroscopy for such purpose was investigated in this study. A batch of 75 pineapple fruits that would have been delivered to a canning factory was tested. Spectra were acquired using a spectrophotometer in interactance mode with wavelengths in the region of 400-2500 nm. Twelve scans of different parts of each pineapple were made. The actual amount of nitrate in the pineapple flesh was determined by HPLC. Original spectra and pretreated spectra were both used to construct calibration models with partial least squares regression (PLSR). The best model was obtained from an average spectrum pretreated with first derivative treatment at the wavelength range of 600-1200 nm. Predictions based on this model matched closely with the actual nitrate contents, with a high correlation coefficient (R) of 0.95 and a low root mean square error of prediction (RMSEP) of 1.77 ppm. These results demonstrate that Vis-NIR spectroscopy can be used for rough screening of intact pineapple.
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    Grouping marian plums harvested at different times by transmittance near-infrared spectroscopy
    (2017-11-25)
    Teerachaichayut, S.
    ;
    Phonmakham, S.
    ;
    Suktanarak, S.
    Marian plum (Bouea burmanica Griff.) ‘Toon Klaow’ is one of Thailand’s favorite fruits. Marian plum’s edible quality depends strongly on its harvest time. This study investigated a non-destructive technique for classifying marian plums according to their harvest time after the day that their blossom set. The non-destructive technique used was transmittance mode, short wavelength near-infrared (SW-NIR) spectroscopy in the wavelength range 660-960 nm. Marian plum samples (n=110) were harvested at 62, 65, 68 and 73 days after flowering. The soluble solids content (SSC) and titratable acid (TA) were determined accurately by standard methods. SW-NIR spectra of these samples were obtained and analyzed by principle component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The following spectral pretreatments were applied, standard normal variate (SNV), smoothing (Savitsky-Golay), and first derivative, in order to obtain optimal grouping results. The PC1 and PC2 score plot of the PCA could not clearly separate some of the classified groups. For the results of PLS-DA, its cross-validated grouping accuracy was R=0.91 and RMSECV=1.28; hence, it can be concluded that SW-NIR spectroscopy has good potential for determining the harvest time of marian plums.