# Rapid Assessment of Rice Quality Traits Using Low-Cost Digital Technologies
## 2 Materials and Methods
### Near-Infrared Spectroscopy and Electronic Nose Measurements  
Triplicate samples of raw rice were used to obtain the NIR absorbance value (1596-2396 nm) at every 7 to 9 nm interval using the microPHAZIR(tm) RX Analyzer (Thermo Fisher Scientific, Waltham, MA, USA). Calibration was conducted before the first scanning and repeated after about 10-15 scans using a standard white reference provided by the manufacturer. The measurement was obtained using a sample holder attached to the top of the scanning area to avoid environmental noise, and the samples were poured into the holder. The NIR measurements were obtained three times per replicate for each sample. The NIR absorbance values were transformed using the first derivative of the Savitsky-Golay method with polynomial order two in The Unscrambler X version 10.3 (CAMO Software, Oslo, Norway).  
A total of 60 g of raw rice samples was transferred into a 500 mL glass beaker to obtain the e-nose sensor readings (Supplementary Materials Figure S1) in triplicates. The e-nose sensor used in the study is portable and was developed by the DAFW-UoM using low-cost materials [61]. It consists of nine sensors (Henan Hanwei Electronics Co., Ltd., China) sensitive to different gases (Table 2). This e-nose has holes between each gas sensor to avoid any oversaturation from volatiles and avoid moisture accumulation. Calibration procedures were performed between each measurement for about 20 s to let the sensors reach the baseline reading before taking the new measurement. The sensors were exposed to the rice samples for one minute to obtain a stable sensor reading from the peak values. The outputs are given in volts and were analyzed using a supervised code in Matlab 2021a (Mathworks Inc., Natick, MA, USA) to detect the stable signals from the sensor readings, followed by ten equidistant subdivisions to obtain ten mean value readings per sensor [25]. The same procedure was repeated for cooked rice samples to obtain the e-nose sensor readings for the 17 rice samples.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Class ID**} & **Product** & \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Brands**} & \multirow{2}{*}{**Abbreviation**} & **Rice-to-Water** & **Cooking Time** \\  & & & & **Ratio (\(\chi\)/\(\
u\))** & **(min)** \\ \hline
12 & \multirow{4}{*}{Biodynamic rice b} & Honest to & \multirow{2}{*}{BDM} & \multirow{2}{*}{1:1 2/3} & \multirow{2}{*}{55} \\  & & Goodness & & & \\ \cline{1-1} \cline{5-5}
13 & & Medium grain b & SunRice & MGB & 1:1 2/3 & 55 \\ \cline{1-1} \cline{5-5}
14 & \multirow{2}{*}{Whole grain rice} & Medium & \multirow{2}{*}{Macro} & \multirow{2}{*}{MOB} & \multirow{2}{*}{1:1 2/3} & \multirow{2}{*}{55} \\  & & grain—organic b & & & \\ \hline
15 & \multirow{2}{*}{(Unpolished rice)} & Doongara c & SunRice & DGR & 1:1 2/3 & 55 \\ \cline{1-1} \cline{5-5}
16 & & Black rice c & SunRice & BKR & 1:1 1/2 & 60 \\ \cline{1-1} \cline{5-5}
17 & \multirow{2}{*}{Wild rice—organic c} & Honest to & \multirow{2}{*}{WRO} & \multirow{2}{*}{1:1 1/3} & \multirow{2}{*}{60} \\  & & GC & Goodness & & \\ \hline \end{tabular}  
* a short grain; b medium grain; c long grain.  
\end{table}
Table 1: _Cont._