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Evaluation of salt content of curry soup containing coconut milk by near infrared spectroscopy
(2018-06-01)
Cheevitsopon, Ekkapong
;
Sirisomboon, Panmanas
A feasibility study was performed to assess whether near infrared spectroscopy could evaluate the salt content of curry soup containing coconut milk. The soup samples were from the mixing tank, a water content adjusted tank, the ultra-high temperature pipe, and laminated containers of a food processor plant. In addition, fish sauce adjusted samples made from the same recipe but with increasing or decreasing (±30%, 60%, and 90%) sauce content were prepared. There were 113 samples in total, which were scanned using a Fourier-transform near infrared spectrometer. The prediction models for salt content were established using near infrared spectral data in conjunction with partial least squares regression. Calibration models developed using all of the samples were validated using leave-one-out cross validation and test set validation. The unadjusted sample models were validated using test set validation. The results showed that both validation methods for the calibration models using all of the samples provided similar model performance where the r2, root mean square error of calibration/root mean square error of prediction, and residual predictive deviation were 0.956, 0.065%, and 4.77 for cross validation and 0.954, 0.064%, and 4.64 for the test set, respectively. However, the salt unadjusted sample model showed better performance where the r2, RMSEP, and RPD were respectively 0.963, 0.043%, and 5.23, indicating that excellent models can be developed to determine the salt content of curry soup containing coconut milk for any applications, including quality assurance.
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Rapid evaluation of fat content in curry soup containing coconut milk by using near infrared spectroscopy
(2018-02-01)
Cheevitsopon, Ekkapong
;
Sirisomboon, Panmanas
The feasibility of a near infrared spectroscopy to evaluate the fat content in instant curry soup containing coconut milk including green curry, red curry, massaman curry and panang curry was investigated. The soup samples were collected from a processing line and as the finished product. There were also fat content-adjusted samples where the curry was made from the same recipe as in the processing line but increasing by 30, 60 and 90% coconut milk and reducing by 30, 60 and 90% coconut milk from normal. A Fourier transform near infrared spectrometer was used to collect scans. A partial least squares regression model for fat content was established using near infrared spectral data in conjunction with reference data, which was validated using a leave-one-out cross-validation and test set validation. The test set validation, using a set of unknown samples, showed better prediction performance. The best model developed using vector normalization spectral pre-treatment on 9404–7498 and 6102–5446 cm−1 provided coefficient of determination, root mean square error of prediction, bias and ratio of performance to interquartile values of 0.90, 0.9%, −0.1% and 1.2, respectively, for the validation samples. However, the model developed using samples without fat content adjusted samples gave a slightly lower coefficient of determination (0.89), but provided a lower root mean square error of prediction (0.5%) and acceptable ratio of standard error of validation to the standard deviation (3.2). In addition, the vibration bands of CH<inf>2</inf> which was in the long chain fatty acid moiety highly influenced the prediction of fat content in the curry soup. The near infrared spectroscopy protocol developed for the determination of fat could be applied in the instant curry soup production line.
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Evaluation of soluble solids of curry soup containing coconut milk by near infrared spectroscopy
(2017-06-01)
Sirisomboon, Panmanas
;
Nawayon, Jutarat
The aim of this research was to do a feasibility study of near infrared spectroscopy to evaluate soluble solids of curry soup containing coconut milk. The soup samples were collected from mixing tanks, water adjusting tanks, an ultra-high temperature process line and laminated cartons. There were also soluble solids adjusted samples by adding or reducing coconut sugar where the curry was made from the same recipe as in the processing line but increasing 30, 60 and 90% coconut sugar and reducing 30, 60 and 90% coconut sugar from normal. There were 119 samples in total. Sample was scanned with an FT-NIR spectrometer. A prediction model for soluble solids was established using near infrared spectral data in conjunction with partial least squares regression. When validated using a set of test samples, the model developed using spectra pretreated by min-max normalization in the range 9403.8–6094.3 cm-1, provided a coefficient of determination (r2), root mean square error of prediction, bias and ratio of performance to interquartile of 0.92, 1.0°Brix, 0.1°Brix and 2.4, respectively. It showed the potential of using near infrared spectroscopy to evaluate soluble solids in curry soup. With further development using more natural samples, a more robust model could be achieved to evaluate soluble solids in curry soup in a processing factory.
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Evaluation of total solids of curry soup containing coconut milk by near infrared spectroscopy
(2016-01-01)
Sirisomboon, Panmanas
;
Nawayon, Jutharat
The aim of this research was to perform a feasibility study of the potential of near infrared (NIR) spectroscopy to evaluate the total solids content of instant curry soups containing coconut milk; these included green curry, red curry, massaman curry and panang curry. The soup samples were collected from mixing tanks, water adjusting tanks, ultra-high temperature process line and laminated cartons. Adjusted samples were made from the same recipe as in the processing line but with the total solids increased by 30%, 60% and 90%, and reduced by 30%, 60% and 90% total solids from normal levels. Each sample was scanned with a Fourier transform NIR spectrometer. A prediction model for total solids was established using NIR spectral data in conjunction with reference data using partial least squares regression, which was validated using leave-one-out validation and test set validation. The test set validation showed better prediction performance as proved by using an unknown sample set. The test set validation model was developed using multiplicative scatter correction of spectra for the 6102-5446.3 cm-1 and 4605.4-4242.9 cm-1 regions, and provided a coefficient of determination for prediction (r2), root mean square error of prediction (RMSEP), bias and ratio of standard error of prediction to the standard deviation (RPD) of 0.92, 0.95%, -0.20% and 3.71, respectively. It was shown that NIR spectroscopy could be applied in an instant curry soup production line for process control and quality assurance.

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