Teerachaichayut, Sontisuk

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

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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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    Transient heat modeling for non-destructive assessment of boiled eggs
    (2023-10-01)
    Sahachairungrueng, Woranitta
    ;
    Tonpho, Pasika
    ;
    Veeradechakul, Mungkarej
    ;
    Rosnim, Thitirat
    ;
    Thompson, Anthony Keith
    It is not possible to differentiate between hard-boiled and soft-boiled eggs after processing. Therefore, transient heat of boiled eggs was tested during the production process in order to develop a model that could be used to differentiate between soft-boiled and hard-boiled eggs. Both types of boiled eggs (N=214) were produced in water at 90 °C for different times and then cooled down. The temperature gradients due to heat transfer during cooling in the ambient air were measured every 30 seconds. Results showed that a dimensionless parameter, called number of transfer units (NTU), changed in relation to time during the cooling process, and it was shown that this could be used as an independent variable. Classification models were established using linear discriminant analysis (LDA) and support vector machine classification (SVMC). Samples were divided into a calibration set (N=150) and a prediction set (N=64). The predictive accuracy of the models using LDA and SVMC for classifying eggs into soft-boiled or hard-boiled was 93.8% and 92.2%, respectively. Therefore, it was concluded that the classification models using LDA and SVMC had potential for use as a non-destructive method for classifying groups of eggs into soft-boiled and hard-boiled that could potentially be used in a commercial situation.
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    Non-Destructive Classification of Organic and Conventional Hens’ Eggs Using Near-Infrared Hyperspectral Imaging
    (2023-07-01)
    Sahachairungrueng, Woranitta
    ;
    Thompson, Anthony Keith
    ;
    Terdwongworakul, Anupun
    ;
    Teerachaichayut, Sontisuk
    Eggs that are produced using organic methods retail at higher prices than those produced using conventional methods, but they cannot be differentiated reliably using visual methods. Eggs can therefore be fraudulently mislabeled in order to increase their wholesale and retail prices. The objective of this research was therefore to test near-infrared hyperspectral imaging (NIR-HSI) to identify whether an egg has been produced using organic or conventional methods. A total of 210 organic and 210 conventional fresh eggs were individually scanned using NIR-HSI to obtain absorbance spectra for discrimination analysis. The physical properties of each egg were also measured non-destructively in order to analyze the performance of discrimination compared with those of the NIR-HSI spectral data. Principal component analysis (PCA) showed variation for PC1 and PC2 of 57% and 23% and 94% and 4% based on physical properties and the spectral data, respectively. The best results of the classification using NIR-HSI spectral data obtained an accuracy of 96.03% and an error rate of 3.97% via partial least squares–discriminant analysis (PLS-DA), indicating the possibility that NIR-HSI could be successfully used to rapidly, reliably, and non-destructively differentiate between eggs that had been produced using organic methods from eggs that had been produced using conventional methods.