# Rapid Assessment of Rice Quality Traits Using Low-Cost Digital Technologies
## 1 Introduction
Electric noses (e-noses) are devices consisting of sensor arrays that are sensitive to volatile compounds, which can mimic the human olfactory system [21]. The sensors are exposed to detect the aroma profile, yielding specific volatile organic compounds (VOC) [22]. Several studies have developed or utilized commercial e-nose sensors in food and beverages, showing their potential as a promising tool for quality assessment to distinguish VOC and aroma profiles. For example, the application of e-noses has been recently reported, such as in rice [23; 24], coffee [25], beer and wine [26; 27], broccoli [28], apples [29], beef and pork [30; 31], rapeseed [32; 33], wheat bread [34], cucumbers [35], and basil [36]. On the other hand, near-infrared (NIR) spectroscopy is a rapid screening tool used to evaluate chemical and physical substances based on the interaction between the organic material and the lights ranging, for example, between 750 and 2500 nm in the electromagnetic region [37; 38]. The technique offers feature information on the C-H, N-H, and O-H vibration bonds that determine the NIR spectra fingerprinting of the organic substances and VOC [39], for which the absorbance values are further correlated with the concentration of the sample [40]. This technique has been used in different studies of the food, beverage, and grain industry, including rice [41], tea [42], sugar [43], beer [27], wine [26], coffee [25], and cassava [44]. However, a limited number of studies are available on the application of e-nose and NIR spectroscopy techniques focused on developing rapid methods using low-cost and portable sensing tools, especially for the rice industry. Machine learning is a reliable and promising modeling tool that has been used for the prediction and automation of tasks in many industries, including medical [45], oil and gas [46], manufacturing [47], automotive [48], agriculture [49], and food [50]. Some specific applications of machine learning in the agriculture and food industries are on grapes [51], wine [52; 53], beer [54; 55], honey [56], and coffee [25], among others. According to previous studies, the application of sensors technology combined with machine learning has demonstrated that this technique efficiently produces high accuracy and robust prediction models [26; 57; 58; 59]. Moreover, compared to other supervised machine learning algorithms, ANNs have been commonly used in agriculture and food studies due to their ability to predict non-linear relationships between inputs and targets [49; 60]. This study aimed to evaluate the use of a handheld NIR spectrometer and a low-cost e-nose device designed and developed by the Digital Agriculture, Food and Wine group from The University of Melbourne (DAFW-UoM) [61] paired with machine learning modeling to classify 17 types of commercial rice (Models 1 and 2) and predict the peak area of rice aromas (Models 3, 4, 6 and 7) and other physicochemical parameters (Model 5). The study provided new insight into measuring the VOC from cooked and uncooked rice samples obtained using a low-cost e-nose for simultaneous qualitative and quantitative analysis of rice quality traits. This approach may benefit the rice industry to perform on-site quality screening, monitoring, inspection, and authentication using the reliable, non-destructive, low-cost, and rapid method.