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Author: search.filters.author.Thompson, Anthony KeithSubject: search.filters.subject.Prediction

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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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    Assessing adulterated pineapple juice concentrate using electrical properties
    (2025-01-01)
    Tantinantrakun, Achiraya
    ;
    Sinsamut, Varisara
    ;
    Apairat, Nuengruthai
    ;
    Smutrakalin, Thirapol
    ;
    Thompson, Anthony Keith
    The fraudulent addition of sugars to pineapple juice concentrate undermines consumer trust and satisfaction. Resistance (R), capacitance (C), dissipation factor (D), inductance (L), quality factor (Q), impedance (Z) and phase angle (θ) in the range of 0.012–200 kHz of juice adulterated with sugar increasing levels from 0 to 95% at 0.5% (w/w) intervals were tested to determine whether they could be used for detecting adulteration in pineapple juice concentrate using a LCR (inductance, capacitance, resistance) meter. A multiple linear regression (MLR) model was developed for predicting the concentration of additive sugars in samples. Linear discriminant analysis (LDA) was used for classifying pure pineapple juice concentrate and pineapple juice concentrate adulterated with added sugars. The most accuracy in the MLR model was obtained from θ, which achieved a correlation coefficient of prediction (R<inf>p</inf>) of 0.977 and a root mean square error of prediction (RMSEP) of 5.88% w/w. From the LDA analysis, the most accurate parameter for classification was C, which yielded a predictive classification accuracy of 94.57%. Therefore, this technique indicates its potential for use in the fruit juice industry a simple method for routinely testing in order to ensure the non-contamination of products offered for sale to consumers.