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Total soluble solids, dry matter content prediction and maturity stage classification of durian fruit using long-wavelength NIR reflectance
(2023-12-01)
Saenphon, Chirawan
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Ditcharoen, Sirirak
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Malai, Chayuttapong
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Saengprachatanarug, Khwantri
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Wongpichet, Seree
The DM and TSS of durian pulp moving on a conveyor belt were measured for their rapid and non-destructive qualities based on quantitative and qualitative measurements. The calibration set and prediction set equaled 209 and 69 pulps, respectively. The quantitative test compared the performance of PLS regression for DM and TSS prediction developed from full wavelength (860–1754 nm) and a few significant variables using SPA, GA, and VIP methods. The qualitative test identified the possibility of maturity stage classification by comparing three supervised machine learning classifiers, namely SVM, random forest (RF), and LDA. Effective models for DM and TSS prediction were developed from second derivatives spectra combined with the GA method, exhibiting r2, SEP, and RPD of 0.85, 4.50%, and 2.64, respectively for DM, and 0.66, 5.15%, and 1.60, respectively, for TSS. The model classifying samples into two distinct groups, namely “reject” and “pass,” utilizing the LDA algorithm, exhibited an impressive accuracy rate of 94.20%, making it a suitable choice for quality assurance purposes. This result indicates that the few effective variables were more efficient than full wavelength and improved model accuracy with greater model stability. Enhancing the classification model could involve data sample balancing in each group, leading to further improvements.
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Application of thermal imaging combined with machine learning for detecting the deterioration of the cassava root
(2023-10-01)
Posom, Jetsada
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Duangpila, Chutatip
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Saengprachatanarug, Khwantri
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Wongpichet, Seree
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Onmankhong, Jiraporn
Freshness is an important parameter that is indexed in the quality assessment of commercial cassava tubers. Cassava tubers that are not fresh have reduced starch content. Therefore, in this study, we aimed to develop a new approach to detect cassava root deterioration levels using thermal imaging with machine learning (ML). An underlying assumption was that nonfresh cassava roots may have fermentation inside that causes a difference in the inner temperature of the tuber. This creates the opportunity for the deterioration level to be measured using thermal imaging. The features (pixel intensity and temperature) that were extracted from the region of interest (ROI) in the form of tuber thermal images were analyzed with ML. Linear discriminant analysis (LDA), k-nearest neighbor (kNN), support vector machine (SVM), decision tree, and ensemble classifiers were applied to establish the optimal classification modeling algorithms. The highest accuracy model was developed from thermal images of cassava roots captured in a darkroom under a control temperature of 25 °C in the measurement chamber. The LDA, SVM, and ensemble classifiers gave the best overall performance for the discrimination of cassava root deterioration levels, with an accuracy of 86.7%. Interestingly, under uncontrolled environmental conditions, the combination of thermal imaging plus ML gave results that were of lower accuracy but still acceptable. Thus, our work revealed that thermal imaging coupled with ML was a promising method for the nondestructive evaluation of cassava root deterioration levels.
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Modified specific gravity method for estimation of starch content and dry matter in cassava
(2021-07-01)
Maraphum, Kanvisit
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Saengprachatanarug, Khwantri
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Wongpichet, Seree
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Phuphuphud, Arthit
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Sirisomboon, Panmanas
An empirical model for the estimation of starch content (SC) and dry matter (DM) in cassava tubers was developed as an alternative method to polarimetry and dry oven. These improved estimation equations were developed based on the specific gravity (SG) method. To improve accuracy, the one hundred-seventy-four sample were obtained from four commercial varieties of cassava in Thailand including KU50, CMR38-125-77, RY9 and RY11, respectively. The age of sample collected from four to twelve months after planting was used in this experiment. The empirical model was created from their relationships between SG obtained from small sample size (~100 g) and its SC and DM. The SG for cassava was strongly correlated with the SC and DM, with values for the coefficient of determination (R2) of 0.81 and 0.83, respectively. The SC showed a high correlation with the DM, with R2 of 0.96. To confirm that the empirical model was effective when applied to other samples, unknown samples collected from another area were tested, and the results showed a standard error of prediction (SEP) of 1.02%FW and 3.49%, mean different (MD) of -0.66%FW, -0.89% for the SC and DM, respectively. Hence, our empirical equation based on a modified SG method could be used to estimate the SC and DM in cassava tubers. It can help breeders to reduce costs and time requirements. Moreover, breeders could be used the methods to evaluate the SC and DM from the tuber formation to harvesting stage and monitoring the changes in SC and DM during breeding.
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A low-cost system for moisture content detection of bagasse upon a conveyor belt with multispectral image and various machine learning methods
(2021-05-01)
Nakawajana, Natrapee
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Lerdwattanakitti, Patchara
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Saechua, Wanphut
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Posom, Jetsada
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Saengprachatanarug, Khwantri
This research aimed to propose an online system based on multispectral images for the real-time estimation of the moisture content (MC) of sugarcane bagasse. The system consisted of a conveyor belt, four halogen bulbs, and a multispectral camera. The MC models were developed using machine learning algorithms, i.e., multiple linear regression (MLR), principal component regression (PCR), artificial neural network (ANN), PCA-ANN, Gaussian process regression (GPR), PCA-GPR, random forest regression (RFR), and PCA-GPR. The models were developed using 150 samples (calibration set) meanwhile the remaining 50 samples were applied as a validation set. The comparison of all developed models showed that the PCA-RFR model achieved better detection with a higher accuracy of MC prediction. The PCA-RFR model showed the best results which were a coefficient of determination of prediction (r2 ) 0.72, root mean square error of prediction (RMSEP) 11.82 wt%, and a ratio of the standard error of prediction to standard deviation (RPD) of 1.85. The results show that this technique was very useful for MC rapid screening of the sugarcane bagasse.
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Rapid elemental composition measurement of commercial pellets using line-scan hyperspectral imaging analysis
(2021-04-01)
Pitak, Lakkana
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Sirisomboon, Panmanas
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Saengprachatanarug, Khwantri
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Wongpichet, Seree
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Posom, Jetsada
The use of biomass pellets as a renewable energy source is increasing, leading to the need for rapid assessment of biofuel pellet quality for production monitoring. The purpose of this work was to use line-scan near-infrared (NIR) hyperspectral image technology coupled with chemometric tools to assess the elemental components of biomass pellets. The parameters influencing model performance were investigated, i.e. wavelength and spectral pretreatment technique. Either full wavelength or partial wavelength selected using interval successive projections algorithm (iSPA) and interval genetic algorithm (iGA) were investigated. Either raw spectra or pretreated spectra were used for model development. The models were developed using partial least squares regression (PLSR). The most effective model for the prediction of carbon (C), hydrogen (H), and nitrogen (N) content was developed using iGA wavelength selection and standard normal variate (SNV) spectral pretreatment and provided the highest accuracy with a coefficient of determination of prediction set (r2<inf>p</inf>) and standard error of prediction (SEP) of 0.83 and 1.33%; 0.84 and 0.17%; and 0.90 and 0.098%, respectively. The model could be used for quality assurance. The S content model was poor and not recommended. The relationship between pellet chemical parameters and reflectance characteristics could be used for predicting C, H, and N of biomass pellets.
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Machine learning-based prediction of selected parameters of commercial biomass pellets using line scan near infrared-hyperspectral image
(2021-02-01)
Pitak, Lakkana
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Laloon, Kittipong
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Wongpichet, Seree
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Sirisomboon, Panmanas
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Posom, Jetsada
Biomass pellets are required as a source of energy because of their abundant and high energy. The rapid measurement of pellets is used to control the biomass quality during the production process. The objective of this work was to use near infrared (NIR) hyperspectral images for predicting the properties, i.e., fuel ratio (FR), volatile matter (VM), fixed carbon (FC), and ash content (A), of commercial biomass pellets. Models were developed using either full spectra or different spatial wavelengths, i.e., interval successive projections algorithm (iSPA) and interval genetic algorithm (iGA), wavelengths and different spectral preprocessing techniques. Their performances were then compared. The optimal model for predicting FR could be created with second derivative (D2) spectra with iSPA-100 wavelengths, while VM, FC, and A could be predicted using standard normal variate (SNV) spectra with iSPA-100 wavelengths. The models for predicting FR, VM, FC, and A provided R2 values of 0.75, 0.81, 0.82, and 0.87, respectively. Finally, the prediction of the biomass pellets’ properties under color distribution mapping was able to track pellet quality to control and monitor quality during the operation of the thermal conversion process and can be intuitively used for applications with screening.
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Predicting marian plum fruit quality without environmental condition impact by handheld visible-near-infrared spectroscopy
(2020-11-03)
Posom, Jetsada
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Klaprachan, Junjira
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Rattanasopa, Kamonpan
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Sirisomboon, Panmanas
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Saengprachatanarug, Khwantri
Handheld near-infrared spectroscopy was used to study the effect of integration time and wavelength selection on predicting marian plum quality including soluble solids content (SSC), the potential of hydrogen ion (pH), and titratable acidity (TA). For measurements representing actual conditions, the on-tree fruits were scanned under in-field conditions. The assumption was that the robust model might be achieved when the models were developed under actual conditions. The results of the main effect test show that the integration time did not statistically affect SSC, pH, and TA predictions (p-value > 0.05) and the wavelength range had a significant impact on prediction (p-value < 0.01). An integration time of 30 ms coupled with a wavelength range of 670-1000 nm was the optimal conditions for the SSC prediction, while an integration time of 20 ms with 670-1000 nm wavelength was optimal for pH and TA prediction because of the lowest root-mean-square error of cross-validation (RMSECV). The optimal models for SSC, pH, and TA could be improved using spectral pre-processing of multiplicative scatter correction. The effective models for SSC, pH, and TA improved and reported the coefficients of determination (r2) and root-mean-square errors of prediction (RMSEP) of 0.66 and 0.86 °Brix; 0.79 and 0.15; and 0.71 and 1.91%, respectively. The SSC, pH, and TA models could be applied for quality assurance. These models benefit the orchardist for on-tree measurement before harvesting.
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Two different portables visible-near infrared and shortwave infrared region for on-tree measurement of soluble solid content of marian plum fruit
(2020-01-01)
Posom, Jetsada
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Soonnamtiang, Navavit
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Kotethum, Patcharapong
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Konjun, Pakhpoom
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Sirisomboon, Panmanas
The goal of this study was to predict the soluble solid content (SSC) of on-tree Marian plum fruit using two different wavelength range and algorithm. One of these was the commercial dispersion NIR spectrometer (MicroNIR 1700), providing shortwave infrared (SWIR), while the other was a making diode array spectrometer giving visible-near infrared (Vis-NIR). To search optimal model, the analytical ability of the two wavelength ranges spectrometers coupled with two algorithms: i.e. partial least squares regression (PLSR) and support vector machine regression (SVR), were investigated. Different spectral pre-processing methods were tested. The model providing the lowest root mean square errors of prediction (RMSEP) was selected. Overall, the proposed outcome was that the performance of SWIR was more accurate than Vis-NIR spectrometer, and that both SWIR and Vis-NIR coupled with PLSR algorithm had a higher accuracy than SVR algorithm. The best model for on-tree evaluation SSC was the SWIR constructed using the PLSR algorithm with the spectral pre-processing of the 2nd derivative, providing a coefficient of determination of calibration set (R2) of 0.81, a coefficient of determination of validation set (r2) of 0.76, RMSEP of 0.69 °Brix, and a relative standard error of prediction (RSEP) of 4.43%. The outcome showed that a portable SWIR spectrometer developed with PLSR could be used for monitoring the SSC of individual Marian plum fruit on-tree for quality assurance.
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Investigation of the influence of image acquisition condition on the image repeatability for online measurement of cane bagasse quality using multispectral image
(2019-09-09)
Lerdwattanakitti, Patchara
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Posom, Jetsada
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Saengprachathanarug, Khwantri
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Wongpichet, Seree
On-line measurement of cane bagasse is needed for its utilization. This study aims to investigate effect of the measurement condition of multispectral image for on-line systems. Two main factors (belt speeds and installation angles of light source) influencing the on-line image collection were studied. Four and two levels of the belt speeds (5, 10, 15, and 20 cm/s) and the installation angles (30 and 45°) were specified in this work, respectively. The effect of these two factors on image repeatability was analyzed by ANOVA using a significant level of 5%. The result showed that belt speeds and angles of light source did not influence image repeatability. However, we found that light source angle of 30 degree gave higher precision than 45 degrees

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