Teerachaichayut, Sontisuk

Preferred name

Alternative Name

Teerachaichayut, S.

Main Affiliation

School of Food Industry

sontisuk.te@kmitl.ac.th

Now showing 1 - 10 of 14

Some of the metrics are blocked by your
consent settings
Qualitative analysis for sweetness classification of longan by near infrared hyperspectral imaging
(2019-10-25)
Sahachairungrueng, W.
;
Teerachaichayut, S.
Near infrared analysis is a nondestructive technique used for determining the quality of various materials including fruit and other food. The objective of this study was to test whether near infrared hyperspectral imaging could be used for classifying sweetness of longan. One hundred and twenty samples were divided into a calibration set (n = 80) and a prediction set (n = 40). The average absorbance spectra from samples in the wavelength range of 935-1720 nm were used in this study. The sweetness of longan was represented by total soluble solids (TSS) which was used to separate fruit into a low sweet (TSS≤ 21.30°Bx) and high sweet fruit (TSS> 21.30°Bx). A classification model was developed in order to classify groups of longan based on sweetness, where 0 = low sweet and 1 = high sweet, by partial least squares discriminant analysis (PLS-DA). Spectra were preprocessed using a Savitzky-Golay smoothing method in order to obtain the optimal performance of the classification model. The results showed an accuracy of the classification model in the calibration set of 85% and the accuracy was 77.5% in the prediction set. Therefore, it was concluded that near infrared hyperspectral imaging has a potential for classifying longans nondestructive based on sweetness.
Some of the metrics are blocked by your
consent settings
Detection of internal mold infection in tomato by transmittance near infrared spectroscopy
(2014-10-20)
Jannok, P.
;
Petcharaporn, K.
;
Teerachaichayut, S.
Alternaria alternata is the black mold occurring inside tomato. This defect can be normally found by destructive method but it cannot be detected by visible inspection from outside appearance of intact tomato. Therefore, a non-destructive technique for prediction of internal mold infection in tomato is required. Near infrared (NIR) spectroscopy technique was considered in this research. Transmittance NIR spectra in the range of 665-955 nm of tomato were acquired. Partial least squares-discriminant analysis (PLS-DA) was performed to establish the calibration model. Results indicated that combination of the standard normal variate transformation (SNV) and smoothing (Savitzky-Golay) pretreatment appeared the best method to develop the model. The calibration model was crossvalidated by a training set (N=140) and used for prediction by a test set (N=60). It obtained 85.0% (corrected 88.7% in normal samples and corrected 81.2% in defected samples) and 91.7% (corrected 100% in normal samples and corrected 83.9% in defected samples) of the total accuracy for calibration and prediction, respectively. Moreover, defected samples were classified in 3 levels of infection severity. The accuracies of cross validation for groups of low, medium and high infection severity were investigated and obtained 82.2, 82.4 and 90.0%, respectively. In conclusion, the calibration model from transmittance NIRS technique can be applied for rapid and non-destructive sorting of internal mold infection in intact tomato.
Some of the metrics are blocked by your
consent settings
Development of calibration models to predict texture and total soluble solids in jelly using hyperspectral imaging
(2018-11-09)
Onnom, Poonnada
;
Teerachaichayut, Sontisuk
The quality of foods for the elderly is important which must be controlled in the manufacturing process. A nondestructive technique that is fast, accurate and reliable is required in order to produce the acceptable products for consumers. Hyperspectral imaging technique was used to establish the calibration models for texture and total soluble solids (TSS) of jelly using partial least squares regression. A set of 99 samples were used for calibration and a set of 49 samples were used for prediction. Spectral pretreatments were investigated in order to develop the highest efficiency of the calibration models. The standard normal variate (SNV) spectral pretreatment was selected for development of the model for texture while the smoothing spectral pretreatment was selected for the TSS model. The accuracy of the calibration models for texture obtained the coefficient of determination (R2) of 0.882 and the root mean square error of prediction (RMSEP) of 0.04N and the accuracy of the calibration models for TSS obtained R2 of 0.969 and RMSEP of 1.32°Bx. The results showed that HSI can be used for nondestructive determination of texture and TSS of jelly. It can be applied for an online sorting system for the manufacturing process.
Some of the metrics are blocked by your
consent settings
Interactance and reflectance near infrared spectroscopy for freshness evaluation of hen eggs
(2018-10-05)
Suktanarak, S.
;
Teerachaichayut, S.
;
Jannok, P.
;
Supprung, P.
Haugh units is an important index for evaluate freshness of hen eggs. High score of Haugh units (≥60) from eggs means those are new fresh eggs. This research is aimed to use near infrared spectroscopy for nondestructive prediction of egg's freshness by quantitative evaluation based on Haugh units. Interactance mode (588-1091 nm) and reflectance mode (1000-2500 nm) of near infrared spectroscopy were investigated in this research. Hen eggs from farm in Thailand were studied by storage at 25°C for 21 days. Samples were taken for measurements at different days of storage (0, 4, 7, 10, 14, 18 and 21 days). A set of 247 samples (165 for calibration and 82 for prediction) was used for interactance mode and a set of 150 samples (102 for calibration and 48 for a prediction) was used for reflectance mode. Calibration models were established and cross-validated using partial least squares regression (PLSR). The accuracies were considered by test in prediction groups. The results showed that the interactance obtained better accuracy for prediction (correlation coefficient, R=0.91 and root mean square error prediction, RMSEP=5.64) when compared with reflectance mode (R=0.83 and RMSEP=7.11). In this study, the interactance near infrared spectroscopy is more suitable to use in application for freshness sorting of hen eggs.
Some of the metrics are blocked by your
consent settings
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.
Some of the metrics are blocked by your
consent settings
Quantitative and qualitative assessment of pork meatball containing borax using near infrared spectroscopy
(2018-11-09)
Sukthanaruk, Sineenart
;
Boonpiam, Sirikarn
;
Teerachaichayut, Sontisuk
Borax has been used as a food additive for many years to improve theshelf-life and textureof products including pork meatball. However, it has been shown to have toxic effects for consumers, depending on concentration, and its use as an additive has been banned in many countries. Reflectance near infrared spectroscopy (NIRS) in the wavelength range of 680-2500 nm was tested for predictingboraxqualitatively and quantitatively in pork meatball to which borax had been added at 0, 50, 100 and 300 ppm. Partial least squares regression (PLSR) was used to develop the calibration model. A test showed the accuracy of the prediction with mean coefficient of correlation (R) of 0.980, the root mean square error of prediction (RMSEP) of 21.70 ppm. Partial least square-discriminant analysis (PLS-DA) was used for developing the classification model to distinguish between meatballswhere borax was added and those where no borax was added. These comparisons gave a classification accuracy of 96.91% between 0 and 50 ppm, 99.69%between 0 and 100 ppm and 100% between 0 and 300 ppm. It was concluded that NIRS, which is a nondestructive technique, has the capability to detect borax in meatballs accurately.
Some of the metrics are blocked by your
consent settings
Quantification of acidity and total soluble solids in guavas by near infrared hyperspectral imaging
(2018-11-09)
Klinbumrung, Nutsinee
;
Teerachaichayut, Sontisuk
In order to provide premium quality for marketing of guavas the titratable acidity (TA) and total soluble solids (TSS) levels should be determined. A reflectance near infrared hyperspectral imaging (NIR-HSI) unit in the wavelength range of 936-1696 nm, which is a nondestructive technique, was tested for use in predicting TA and TSS. Samples of 100 guavas were scanned by NIR-HIS as a group for calibration (N=67) and as a group for prediction (N=33). The average spectra from the region of interest (ROI) of samples were used to establish the calibration models for TA and TSS by using partial least squares regression (PLSR) to establish calibration models. The calibration model for TA gave a coefficient of determination (R2) of 0.972 and the root mean square error of prediction (RMSEP) of 0.010% and for TSS the R2 was 0.801 and the RMSEP was 0.437°Bx. The accuracies of these results indicate that NIR-HSI has potential for use in measuring TA and TSS of guavas.
Some of the metrics are blocked by your
consent settings
Non-destructive detection of internal mold infection in sweet tamarind using short wavelength near infrared spectroscopy
(2014-10-20)
Teerachaichayut, S.
;
Suktanarak, S.
;
Kasemsumram, S.
Internal quality of sweet tamarind ('Prakaytong') is an essential commercial attribute. Determination of internal mold cannot be done by visual inspection on the outside of an intact tamarind. Therefore, a non-destructive measurement and data evaluation technique were considered using short wavelength near infrared (SW-NIR) transmittance spectroscopy in order to detect internal mold infection in sweet tamarind. A set of 176 tamarind samples (a calibration set = 124 and a prediction set = 52) were used in this research. Spectra in the region of 665-955 nm were acquired from scanning the center of each seed pod. The averaged spectral reading was used for partial least squares-discriminant analysis (PLS-DA) to establish a classification model for tamarind quality between groups of normal and defected samples. The calibration model obtained optimal result by cross validation using second derivative spectral pretreatment. The classification accuracy on the calibration set was 86.3% (58 out of 62 for the normal samples and 49 out of 62 for the defected samples) and on the prediction set was 84.6% (26 out of 26 for the normal samples and 18 out of 26 for the defected samples). The results showed that SW-NIR transmittance spectroscopy can be used to non-destructively detect internal mold infection in intact sweet tamarind.
Some of the metrics are blocked by your
consent settings
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.
Some of the metrics are blocked by your
consent settings
Non-destructive prediction of moisture content of lime (Citrus aurantifolia Swingle 'Paan') by multiple regression analysis of its electrical and physical properties
(2017-03-21)
Huong, H. T.
;
Teerachaichayut, S.
Large quantity of juice is an important index of lime quality that consumers seek for. Therefore, a non-destructive technique for prediction of lime juice quantity is needed. In this study, moisture content (MC) of lime which is an indicator of its juice quantity was predicted by multiple regression analysis of its electrical properties -capacitance (C), inductance (L) and impedance (Z) at various frequencies (0.012, 0.05, 0.1, 0.2, 5, 10, 20, 50, 100 and 200 kHz) - and physical parameters - weight and geometric mean diameter (GMD). Samples (n=82) were divided into a calibration set (n=55) and a prediction set (n=27). A calibration model for moisture content of lime was established and cross-validated by partial least squares regression (PLSR). Prediction results achieved a coefficient of determination (R2) of 0.934 and a root mean square error of prediction (RMSEP) of 1.822% wet basic, demonstrating that this technique has a real potential for development into a practical non-destructive lime screening method.

Teerachaichayut, Sontisuk

Filters

Settings

Sort By

Results per page