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Start date: 2020Subject: search.filters.subject.Spectra

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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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    Near-infrared hyperspectral imaging for predicting the quality of SO2 pre-treated and dehydrated mango
    (2025-08-01)
    Aozora, Wayan Dipasasri
    ;
    Tantinantrakun, Achiraya
    ;
    Thompson, Anthony Keith
    ;
    Teerachaichayut, Sontisuk
    Prediction for quality indices of SO<inf>2</inf> pre-treated and dehydrated mango was accessed by NIR-HSI. Models for predicting TSS and SO<inf>2</inf> content achieved R = 0.82; RMSEP = 2.42% and R = 0.83; RMSEP = 56.40 mg/kg, respectively. Visualization of TSS and SO<inf>2</inf> content could be presented by predictive images.
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    Nondestructive evaluation of SW-NIRS and NIR-HSI for predicting the maturity index of intact pineapples
    (2023-01-01)
    Tantinantrakun, Achiraya
    ;
    Sukwanit, Supawan
    ;
    Thompson, Anthony Keith
    ;
    Teerachaichayut, Sontisuk
    Determination of optimum maturity and ripeness of fruit is essential in the production of processed fruit, including pineapples, but this is difficult to achieve consistently by visual grading in commercial factories. Therefore, this study tested two nondestructive techniques for predicting the maturity index of intact pineapple. These were transmittance short wavelength near infrared spectroscopy (SW-NIRS) in the wavelength range of 665–955 nm and reflectance near infrared hyperspectral imaging (NIR-HSI) in the wavelength range of 935–1720 nm. The number of samples used for calibration was 120 for both SW-NIRS and NIR-HSI. The maturity index and spectral information of individual pineapple fruit were acquired from both techniques and analysed using the same procedure. Then, partial least squares regression (PLSR) was used to establish the models for predicting the maturity index of each intact fruit. The leave-one-out cross validation was used for evaluating the performance of the models. The results showed that both techniques gave reliable performance in predicting the maturity index of individual fruit, with a coefficient of determination considering cross validation (R<inf>cv</inf><sup>2</sup>) for the prediction of the maturity index of 0.70 and a root mean square error in cross validation (RMSECV) of 2.16 when using SW-NIRS and R<inf>cv</inf><sup>2</sup> of 0.72 and RMSECV of 1.68 when using NIR-HSI. It was therefore concluded that both SW-NIRS and NIR-HSI had the potential for use in nondestructive analysis of the maturity of intact pineapple fruit in fruit processing factories.
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    Prediction of water activity in mamón (Filipino sponge) cakes by near infrared hyperspectral imaging
    (2020-01-01)
    Sricharoonratana, Manunchaya
    ;
    Teerachaichayut, Sontisuk
    Water activity in foods can result in detrimental microbial activity during storage. The usual methods of water activity measurement involve destruction of the sample. Near infrared (NIR) hyperspectral imaging has previously been successfully used as a non-destructive method to determine various physical and chemical characteristics of a variety of foods. Therefore, this method was tested to determine whether it could be used to measure water activity of mamón cakes, a popular sponge cake developed in the Philippines. Individual samples (n = 178) were divided into a calibration set (n=119) and a prediction set (n=59). These samples were tested using NIR hyperspectral imaging (935-1720 nm) with a smoothing spectral pretreatment selected for developing the calibration model. Partial least squares regression was used to establish the model in order to predict the water activity. The results showed the accuracy of the calibration model in prediction that gave a correlation coefficient of 0.767 and the root mean square error of prediction of 0.0130. It was therefore concluded that NIR hyperspectral imaging has a potential for use and application for measuring the water activity of mamón cakes.