## 4 Conclusions  
The results obtained herein for different rice varieties show that NIR spectroscopy in combination with machine learning algorithms, such as ANN, is suitable for the development of prediction models for rice pasting properties. This represents a promising approach to estimating rice quality and is considered an interesting advancement for industry and consumers. The strategy developed in this study could be applied to other systems, allowing for the evaluation of physicochemical parameters of commercial interest and saving time and resources in the process. Conceptualization, P.S.S. and C.B. ; methodology, P.S.S., B.C. and C.B. ; software, P.S.S. ; validation and formal analysis, P.S.S. ; investigation, P.S.S., B.C. and C.B. ; resources, C.B. ; writing--original draft preparation, P.S.S. ; writing--review and editing, P.S.S., B.C. and C.B. ; visualization, P.S.S. ; supervision, C.B. ; project administration, C.B. ; funding acquisition, C.B. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{l c c c c} \hline Rice Type & Breakdown (cP) & iPLS & siPLS & ANN \\ \hline \hline \multicolumn{1}{l}{Sprint} & 957 & 958 & 957 & 952 \\ \multicolumn{1}{l}{Sprint} & 941 & 940 & 941 & 936 \\ \multicolumn{1}{l}{OP 1203-Ceres} & 1654 & 1735 & 1665 & 1673 \\ \multicolumn{1}{l}{OP 1203-Ceres} & 1748 & 1840 & 1760 & 1770 \\ \multicolumn{1}{l}{ARIETE 104} & 1249 & 1284 & 1254 & 1254 \\ \multicolumn{1}{l}{ARIETE 105} & 1242 & 1276 & 1247 & 1247 \\ \hline \multicolumn{1}{l}{Rice type} & Final Viscosity (cP) & iPLS & siPLS & ANN \\ \hline \multicolumn{1}{l}{Sprint} & 3235 & 3248 & 3292 & 3238 \\ \multicolumn{1}{l}{Sprint} & 3261 & 3277 & 3323 & 3266 \\ \multicolumn{1}{l}{OP 1203-Ceres} & 3143 & 3146 & 3182 & 3139 \\ \multicolumn{1}{l}{OP 1203-Ceres} & 3249 & 3263 & 3309 & 3253 \\ \multicolumn{1}{l}{ARIETE 104} & 3080 & 3077 & 3107 & 3072 \\ \multicolumn{1}{l}{ARIETE 105} & 3051 & 3044 & 3072 & 3041 \\ \hline \multicolumn{1}{l}{Rice type} & Peak Viscosity (cP) & iPLS & siPLS & ANN \\ \hline \multicolumn{1}{l}{Sprint} & 2235 & 2215 & 2219 & 2201 \\ \multicolumn{1}{l}{Sprint} & 2264 & 2245 & 2253 & 2232 \\ \multicolumn{1}{l}{OP 1203-Ceres} & 3229 & 3241 & 3339 & 3248 \\ \multicolumn{1}{l}{OP 1203-Ceres} & 3401 & 3418 & 3531 & 3428 \\ \multicolumn{1}{l}{ARIETE 104} & 2774 & 2772 & 2826 & 2769 \\ \multicolumn{1}{l}{ARIETE 105} & 2745 & 2742 & 2793 & 2738 \\ \hline \multicolumn{1}{l}{Rice type} & Setback (cP) & iPLS & siPLS & ANN \\ \hline \multicolumn{1}{l}{Sprint} & 1000 & 1075 & 1032 & 1010 \\ \multicolumn{1}{l}{Sprint} & 997 & 1071 & 1028 & 1007 \\ \multicolumn{1}{l}{OP 1203-Ceres} & \(-87\) & \(-103\) & \(-98\) & \(-102\) \\ \multicolumn{1}{l}{OP 1203-Ceres} & \(-152\) & \(-173\) & \(-166\) & \(-169\) \\ \multicolumn{1}{l}{ARIETE 104} & 306 & 323 & 310 & 299 \\ \multicolumn{1}{l}{ARIETE 105} & 306 & 322 & 309 & 299 \\ \hline \multicolumn{1}{l}{Rice type} & Trough (cP) & iPLS & siPLS & ANN \\ \hline \multicolumn{1}{l}{Sprint} & 1278 & 1278 & 1258 & 1265 \\ \multicolumn{1}{l}{Sprint} & 1323 & 1325 & 1306 & 1310 \\ \multicolumn{1}{l}{OP 1203-Ceres} & 1576 & 1583 & 1578 & 1561 \\ \multicolumn{1}{l}{OP 1203-Ceres} & 1652 & 1661 & 1660 & 1638 \\ \multicolumn{1}{l}{ARIETE 104} & 1525 & 1531 & 1523 & 1511 \\ \multicolumn{1}{l}{ARIETE 105} & 1503 & 1509 & 1499 & 1489 \\ \hline \end{tabular}
\end{table} 
Table 4: Pasting properties predicted using the various developed models. Funding: Funding for this research was received from TRACE-RICE--Tracing rice and valorizing side streams along with Mediterranean blockchain, grant no. 1934 (call 2019, Section 1 Agrofood)--of the PRIMA Program supported under Horizon 2020, the European Union's Framework Program for Research and Innovation. This work was also supported by FCT, the Portuguese Foundation for Science and Technology through the R&D Unit, UIDB/04551/2020 (GREEN-IT, Bioresources for Sustainability) and project UIDB/04033/2020. [PERSON].