Rapid Assessment of Rice Quality Traits Using Low-Cost Digital Technologies

Abstract

Aroma and other physicochemical parameters are important attributes influencing consumer perception and acceptance of rice. However, current methods using multiple instruments and laboratory analysis make these assessments costly and time-consuming. Therefore, this study aimed to assess rice quality traits of 17 commercial rice types using a low-cost electronic nose and portable near-infrared spectrometer coupled with machine learning (ML). Specifically, artificial neural networks (ANN) were used to classify the type of rice and predict rice quality traits (aromas, color, texture, and pH of cooked rice) as targets. The ML models developed showed that the chemometrics obtained from both sensor technologies successfully classified the rice (Model 1: 98.7%; Model 2: 98.6%) and predicted the peak area of aromas obtained by gas chromatography-mass spectroscopy found in raw (Model 3: R = 0.95; Model 6: R = 0.95) and cooked rice (Model 4: R = 0.98; Model 7: R = 0.96). Furthermore, a high R = 0.98 was obtained for Model 5 to estimate the color, texture, and pH of cooked rice. The proposed method is rapid, low-cost, reliable, and may help the rice industry increase high-quality rice production and accelerate the adoption of digital technologies and artificial intelligence to support the rice value chain.

Rapid Detection of Fraudulent Rice Using Low-Cost Digital Sensing Devices and Machine Learning

“…On the other hand, MG811 was the sensor sensitive to carbon dioxide and had the highest FL in PC2. These results reflect those of a prior study by Aznan et al [11], who also found significant positive correlations (p < 0.05) between MQ137 and volatile compounds found in raw rice, such as the valeric anhydride (r = 0.53), nonanal (r = 0.49), and octanoic acid (r = 0.54). Octanoic acid is a short-chain fatty acid found in raw rice that was developed through the oxidation of linoleic acid over the storage period [31], while nonanal is one of the major VOCs that contribute to the rice aroma associated with aldehydic, fatty, waxy, citrus, and floral aromas [32].…”

“…Compared to GC-MS analysis, an e-nose provides rapid detection to obtain results in a few seconds or minutes. Previous studies in rice have shown the reliability of e-nose applications such as analyzing the volatile compounds in rice [11], distinguishing expired and non-expired rice [12], monitoring rancidity and insect infestation in brown rice [13], detecting fungal infection in jasmine brown rice [14], and identifying moldy rice [15]. In rice adulteration, Udomkun et al [16] assessed the feasibility of a commercial e-nose paired with principal component analysis (PCA) to identify the degree of adulteration in Thai jasmine rice in storage conditions.…”

Modeling Textural Properties of Cooked Germinated Brown Rice Using the near-Infrared Spectra of Whole Grain

“…PLSR and PCR were combined with the NIR spectra for the evaluation of the hardness and toughness of cooked parboiled rice, respectively [22]; however, the models were only applicable for screening and approximate calibration. The artificial neural networks (ANNs) combined with the NIR spectra of raw rice provided a model with a high R = 0.94; this combination could estimate the color, texture, and pH of cooked rice, offering a rapid and cost-effective method [23]. Together, these findings highlight the potential of digital technologies, artificial intelligence, and spectroscopy to enhance rice quality assessment efficiently and precisely.…”

“…The back extrusion (BE) test rig developed by researchers in the Asian Institute of Technology has been used for measuring the hardness of cooked rice [33,36,37], and the BE instrument could best interpret the sensory hardness-softness texture of cooked rice [36]. The BE testing of the textural properties of cooked GBR rice showed a high measurement precision in the hardness, toughness, and stickiness tests, respectively [23]. Therefore, we used the BE for our experiment.…”

“…ANNs have shown their outperformance compared to PLS regression in the estimation of the textural properties of cooked rice by the models developed by using the spectra of whole grain. Aznan et al [23] used a portable near-infrared spectrometer coupled with an ANN to predict the rice quality traits (color, texture, and pH of cooked rice) of 17 commercial rice types, and a high correlation coefficient (R) of 0.94 was obtained. The prediction of the hardness and toughness of cooked parboiled rice by using the FT-NIR spectra of whole grain parboiled rice combined with PLS regression and principal component regression (PCR) provided the r 2 values of prediction of 0.70 and 0.66, respectively, which indicated the lower performance of the PLS regression.…”