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Feasibility of a photoelectric sensor technique for nondestructive prediction of granulation disorder in tangerines
(2019-10-25)
Teerachaichayut, S.
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Kijpadung, T.
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Cheevathumrat, V.
Granulation is a physiological disorder in Tangerine (Citrus reticulata) that can cause an internal disorder which cannot be determined by visual inspection. A nondestructive technique, which is simple and low cost, that could determine whether an individual fruit suffered from granulation would be of help to the citrus industry. The intensity signal of photoelectric sensor, when light passed through tangerines 'Keaw Dumnuan' was investigated and showed that the signal was significantly larger (p < 0.05) for normal tangerines and those suffering from granulation. In order to determine a suitable non-destructive method for detect granulation disorder in tangerines that could be applied commercially, a simple and low cost prototype of this photoelectric sensor technique was developed. A set of 52 fruit were used and their average intensity signal from four measurements was used for discriminant analysis. The cut off value of 61.7 mV was used for classification. If a fruit had the signal intensity equal to or less than 61.7 mV, it had granulation, while fruit that had the signal intensity of more than 61.7 mV was normal. Using this signal intensity discrimination the accuracy of classification was 90.4%. It was also shown that the accuracy of prediction was related to the severity of granulation in each fruit. Therefore, the photoelectric sensor technique was shown to be feasible for use in nondestructively detect of granulation in tangerines and be possibly used in future grading systems.
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Qualitative analysis for sweetness classification of longan by near infrared hyperspectral imaging
(2019-10-25)
Sahachairungrueng, W.
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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.
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Feasibility of using a photoelectric sensor combined with density measurements for nondestructive assessment of the freshness of Hen's eggs
(2018-11-09)
Teerachaichayut, S.
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Pansiri, J.
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Nguanprasert, P.
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Thepwapee, W.
Quality of hen's eggs relates to their storage time. Freshness of hen's eggs is an important quality but it cannot be determined by a visual inspection, the only way is to take a sample from a batch of eggs and test the sample destructively by the internationally accepted Haugh method. This study used a photoelectric sensor in order to test whether it could be used as a nondestructive test to detect the freshness of hen's eggs when combined with measurement of their density by displacement in water. Eggs were stored at 25°C for up to 20 days and each egg was tested at intervals first with the photoelectric sensor, then their density was measured and finally each egg was broken and subject to the Haugh test. A training set (N=64) was used to establish a model for predicting the freshness of the eggs based on storage time using multiple linear regression. A test set (N=32) was used to test the model. The model had a coefficient of determination (R<sup>2</sup>) of 0.881 and the root mean square error of prediction (RMSEP) of 2.36 days. This showed that the photoelectric sensor combined with density measurements could be used to develop a model that gave reliable results for nondestructive prediction of the freshness of hen's eggs and how this changed during storage.
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Quantitative analysis of quality for marian plum (Bouea burmanica Griff.) by transmittance near infrared spectroscopy
(2018-10-05)
Phonmakham, S.
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Suttivijitpukdee, N.
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Teerachaichayut, S.
Marian plum (Bouea burmanica Griff.) is one of the most popular tropical fruits in Thailand. The good quality of marian plum is required by consumers. Total soluble solid (TSS) and titratable acidity (TA) are important indices for consideration of quality for marian plum. Transmittance mode of near infrared (NIR) spectroscopy in the short wavelength (665-955 nm) was considered for nondestructive evaluation of quality in marian plum. A set of 153 marian plums (105 samples for a calibration group and 48 samples for a prediction group) was carried out in this research. The partial least squares regression (PLSR) was used to develop the calibration models. Spectral pretreatments were investigated in order to obtain the best performance of the models. A calibration model for TSS using original spectra obtained best results for calibration and prediction (R=0.90, RMSEC=0.57 °Bx and R=0.88, RMSEP=0.65 °Bx, respectively). As well as the calibration model for TA using original spectra obtained best results for calibration and prediction (R=0.98, RMSEC=0.01% and R=0.88, RMSEP=0.03%, respectively). All results indicated that it is possible to use transmittance SW-NIRS for nondestructive prediction of TSS and TA in marian plums.
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Interactance and reflectance near infrared spectroscopy for freshness evaluation of hen eggs
(2018-10-05)
Suktanarak, S.
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Teerachaichayut, S.
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Jannok, P.
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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.
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Non-destructive prediction of pH and total soluble solids of lime [Citrus × aurantifolia (Cristm.) Swinge] by visible and near-infrared spectroscopy
(2017-11-25)
Huong, H. T.
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Teerachaichayut, S.
The non-destructive visible and near-infrared spectroscopy (Vis/NIRS) technique is well suited for evaluating various internal quality indices of fruits quickly and accurately. The objective of this study was to evaluate the relationships between Vis/NIR measurements and the internal quality indices of lime, including pH and total soluble solids (TSS, °Brix). For this experiment, reflectance measurement in the 400-2500 nm range was done on 140 samples for pH and 117 samples for TSS. Partial least square (PLS) regression was used to establish the calibration models. First-order derivative and multiplicative scatter correction spectral pretreatments were used to develop calibration models for pH and TSS, respectively. The correlation coefficient (R) and the root mean square error of prediction (RMSEP) from the calibration model for pH were 0.95 and 0.06. The corresponding values for TSS were 0.81 and 0.24 °Brix, respectively. The results showed that Vis/NIRS measurements in the spectral range 400-2500 nm could be used to access pH and TSS of lime.
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Grouping marian plums harvested at different times by transmittance near-infrared spectroscopy
(2017-11-25)
Teerachaichayut, S.
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Phonmakham, S.
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Suktanarak, S.
Marian plum (Bouea burmanica Griff.) ‘Toon Klaow’ is one of Thailand’s favorite fruits. Marian plum’s edible quality depends strongly on its harvest time. This study investigated a non-destructive technique for classifying marian plums according to their harvest time after the day that their blossom set. The non-destructive technique used was transmittance mode, short wavelength near-infrared (SW-NIR) spectroscopy in the wavelength range 660-960 nm. Marian plum samples (n=110) were harvested at 62, 65, 68 and 73 days after flowering. The soluble solids content (SSC) and titratable acid (TA) were determined accurately by standard methods. SW-NIR spectra of these samples were obtained and analyzed by principle component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The following spectral pretreatments were applied, standard normal variate (SNV), smoothing (Savitsky-Golay), and first derivative, in order to obtain optimal grouping results. The PC1 and PC2 score plot of the PCA could not clearly separate some of the classified groups. For the results of PLS-DA, its cross-validated grouping accuracy was R=0.91 and RMSECV=1.28; hence, it can be concluded that SW-NIR spectroscopy has good potential for determining the harvest time of marian plums.
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Classification of sweet corn based on storage time after harvest using near infrared spectroscopy
(2017-03-21)
Suktanarak, S.
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Supprung, P.
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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.
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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.
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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.
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Detection of hardening pericarp disorder and determination of firmness at hardening area in mangosteen by visible-near infrared reflectance spectroscopy
(2015-01-01)
Workhwa, S.
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Teerachaichayut, S.
Mangosteen (Garcinia mangostona L.) is an economically important fruit grown commercially in Thailand for domestic consumption and export. The fruit has a thick and hard pericarp. However, hardening pericarp disorder can easily occur as a result of compression or impact during harvest and transport. Classification and prediction of hardening pericarp disorder in mangosteen was investigated using visible-near infrared spectroscopy (Vis/NIRS). Reflectance spectra were acquired on each of 1100 mangosteen samples. The number of samples for training and test set was 733 and 367 samples, respectively. Partial least squares-discriminant analysis (PLS-DA) was used for quantitative analysis. The results of discriminant analysis of normal and hardening pericarp samples using leave-one-out cross-validation achieved an average total accuracy of 92.92%. A further goal was quantitative analysis of firmness of mangosteens with hardening pericarp using Vis/NIR measurements. The optimum calibration model was pretreated using standard normal variate transformation (SNV) pretreatment and was developed using partial least squares regression (PLSR). The model was proven useful for prediction of the degree of pericarp hardening of mangosteen. The coefficients of correlation (R) and root mean square error of cross validation (RMSECV) were 0.89 and 2.67N respectively. This technique has potential use for nondestructive and rapid classification of quality for mangosteen.

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