EVALUATION OF THE QUALITY AND CLASSIFICATION OF PARBOILED RICE USING NEAR-INFRARED SPECTROSCOPY AND MULTIVARIATE STATISTICAL ANALYSESABSTRACT
Physical classification is the official standard method for determining grain quality for commercialization. However, it is a time-consuming, subjective operation, susceptible to errors, and requires skilled labor. Optical methods of indirect measurement emerge as a promising evaluation alternative, offering economic advantages, standardization in the assessment of grain nutritional quality, and greater accuracy. Therefore, this study aimed to evaluate the use of near-infrared (NIR) spectroscopy and multivariate statistical analyses to determine the physicochemical quality of parboiled rice grains. Parboiled rice samples were classified according to the Technical Regulation for Rice (Type 1 to Type 5 and Off-Type). Each type was analyzed by NIR to determine the proximate composition (crude protein, moisture, lipids, crude fiber, ash, and starch). The data obtained were subjected to analysis of variance, Tukey's test, Pearson correlation, and principal component analysis. Regarding starch, the main constituent of rice grains, Types 1 and 2 had the highest concentrations (70.11% and 70.16%, respectively), while the lowest concentrations (66.52% and 66.73%) were found in Types 3 and 5, respectively. The results indicated that NIR, combined with multivariate statistical analyses, can be an efficient alternative for characterizing the physicochemical quality of parboiled rice, highlighting clear patterns, especially in starch and fiber content.
physical classification; technological evaluation methods; grain quality for commercialization
INTRODUCTION
Rice is a cereal consumed worldwide and plays a crucial role in human nutrition, serving as a staple food for more than one-third of the global population. It is an excellent energy source due to its high carbohydrate content, particularly starch, as well as proteins, vitamins, essential minerals, and low lipid levels. This cereal provides approximately 20% of the energy and 15% of the protein required by humans, further standing out for its easy digestion (Jayaprakash et al., 2022). White rice, obtained through milling and polishing, remains the most consumed form, accounting for about 70%, while parboiled rice, which undergoes hydrothermal processing, is preferred by 25% of the population (Muchlisyiyah et al., 2023).
Ensuring product quality requires the physical classification of grains, which involves technical and laboratory analyses to determine quality according to commercialization standards. This process identifies groups and types based on observed characteristics. Classification procedures include homogenization, impurity analysis, moisture content determination, and visual inspection of defects, which are recorded in reports that establish the quality standard, according to the Technical Regulation for Rice (Brazil, 2009). On the other hand, this process is time-consuming, subjective, requires skilled labor, and is prone to evaluation errors.
Considering the aforementioned, there is an increasing need for alternative non-destructive technologies to indirectly assess the physicochemical quality of grains in the agro-industrial sector. These technologies aim to optimize operational efficiency and streamline grain batch processing. Optical methods, which rely on indirect measurement, emerge as promising solutions to this pressing demand. In addition to offering long-term economic advantages, operational simplicity, and sample preservation, they provide fast and non-intrusive nutritional quality assessments of grains. These methods reduce or eliminate the need for sample preparation, minimize laboratory expenses, and decrease waste generation (Guan et al., 2022; Gomit et al., 2022).
Among indirect methods, near-infrared spectroscopy (NIR) stands out as it is based on radiant energy absorption and molecular vibration, enabling the rapid determination of the chemical and physical properties of grains without altering or preparing the sample. NIR is an efficient and non-destructive technique for chemical characterization and is widely used in various sectors, including agriculture, food, pharmaceuticals, petrochemicals, and chemistry (Evangelista et al., 2021).
NIR is a well-established tool for characterizing agricultural products and food, having been extensively used by researchers to evaluate grain quality, including soybeans (Jiang, 2020; Chadalavada et al., 2022), corn (Jahangirlou et al., 2021; Sun et al., 2023), and wheat (Hao & Shi, 2023). For rice, NIR has been applied for several purposes, such as determining moisture content using shortwave near-infrared non-destructive spectroscopy (Makky et al., 2019), identifying rice seed varieties (Chen et al., 2019), and as an alternative to conventional classification methods in combination with machine learning algorithms (Carneiro et al., 2023). Jin et al. (2022) used near-infrared spectroscopy (NIR) to analyze various rice seed varieties from different cultivars.
Thus, this study aimed to analyze the physicochemical quality of parboiled rice classified based on physical classification typologies, using near-infrared spectroscopy and multivariate statistical analysis.
MATERIAL AND METHODS
The composite samples of polished parboiled rice were collected immediately after the processing of the grains, without any prior storage period. They were classified into types according to the maximum defect limits tolerated for parboiled rice, as established in Normative Instruction No. 02, dated February 07, 2012, which sets the technical regulation for rice, defining classification standards and quality requirements (Brazil, 2012). The 2 kg sample formed from each type (Table 1) was subdivided into 100 subsamples, each containing 20 g of rice grains from Types 1, 2, 3, 4, 5, and Off-Type.
Description of the parboiled rice samples analyzed in the study, highlighting the percentages of defects by type.
Figure 1 shows the parboiled rice samples, highlighting the differences between each of the types and the off-type.
Parboiled rice samples: A) Type 1; B) Type 2; C) Type 3; D) Type 4; E) Type 5 and F) Off-type.
The samples were subjected to analysis by near-infrared spectroscopy (Metrohm, NIRSTM DS2500 spectrometer, Herisau, Switzerland) to determine the proximate composition, including crude protein (PROT), moisture content (MOI), lipids (LIP), crude fiber (FIB), ash (ASH), and starch (STARCH).
For the analyses, the samples were homogenized and placed in sampling capsules, with three repetitions for each sample. The NIR analysis procedure consisted of illuminating the sample in the capsule with radiation at a specific wavelength in the near-infrared region, and then measuring the difference between the amount of energy emitted by the spectrometer and reflected by the sample to the detector. This difference was measured across several bands, creating a spectrum for each sample. The spectral data was recorded in reflectance mode, in the spectral range of 400 to 2500 nm.
After obtaining the data, they were subjected to analysis of variance, mean test (Tukey), Pearson correlation, and principal component analysis (PCA). The analysis of variance and mean test were performed using the Sisvar 5.8 software. The Pearson correlation network was obtained using Rbio 192 software (Bhering, 2017), with a cutoff value (minimum correlation value used to highlight the lines) set at 0.6. The Principal Component Analysis (PCA) was determined using RStudio software.
RESULTS AND DISCUSSION
Table 2 shows the results of the analysis of variance and the mean test for the variables of the proximate composition based on the types of polished parboiled rice. From the results obtained, a statistical difference (p ≤ 0.05) was observed between the treatments for all the analyzed variables.
The grains that make up Type 1 are characterized by the lowest presence of defects, indicating better quality compared to the other types. The physical classification, in addition to impacting the subsequent processing of rice, such as cooking time, final texture, and nutritional value, directly determines the product's market value (Sharma & Khanna, 2020; Lu et al., 2024).
The moisture content ranged from 11.19% (Type 1) to 12.65% (Type 5), with an average of 11.94%, which is below the minimum limit of 12% of the ideal range to optimize and reduce grain breakage during processing (12-14%) (Alim et al., 2024). This moisture range is crucial to preserve the culinary and milling properties of parboiled rice, also ensuring a longer shelf life of the product (Muchlisyiyah et al., 2023). However, according to some studies, to preserve the quality of rice and allow long-term storage, the grains should be dried to a moisture content of 11–14% (Alhendi et al., 2019; Ramli et al., 2021).
Starch is the main component of rice grains and consists of two large glucose polymers: amylopectin and amylose, being crucial for the technological and consumption quality of rice (Ansah et al., 2023). The highest starch concentrations were found in samples of Type 1 (70.11%) and Type 2 (70.16%), while the lowest starch content was found in the Type 3 sample (66.52%), followed by the Type 5 sample (66.73%). The low starch content found in the grains of Types 3 and 5 was due to the high percentages of defective grains in the sample, mainly with moldy (fungus) and insect-damaged grains, which results in higher energy and nutrient consumption.
The highest protein concentration was observed in Type 5 samples (5.25%), while the lowest was found in Type 1 (4.19%) and Out of Type (4.26%) samples. The higher protein concentration in Type 5 rice was due to the low starch content as a result of the high percentage of broken grains in the sample, as seen in Table 1. In other words, the presence of broken grains decreased the starch content and enhanced the protein content in the analysis, highlighting amino acids (Zhang et al., 2020). The result indicated that the commercialization of rice grains, which is determined by classification, is related to high starch contents, and the higher protein concentrations did not indicate that the samples of grains were of better quality (Chen et al., 2023). Lipids, fibers, and ash are present in smaller amounts in the rice grain and followed the same trend as protein, with higher contents in grains from Type 2 to Off-Type.
In Figure 2, the Pearson correlation network between the centesimal composition variables of Types 1 to Type 5 and Off-type of parboiled rice is presented. The relationship between protein content and starch in rice is linked to genetic and environmental factors (Lou et al., 2023). Nayak et al. (2022), when mapping the association for protein content, total soluble sugars, starch, amylose, and chlorophyll in rice, identified that higher protein levels in rice may be correlated with a lower starch concentration, which supports the negative correlation found with an intensity of -0.705 between STAR and PRO.
Pearson correlation network between the proximate composition variables of Types 1 to Type 5 and Off-type parboiled rice. Green lines link positively correlated variables, while red lines link negatively correlated variables. The line thickness is proportional to the magnitude of the correlation. MOI = Moisture; STA = Starch; PRO = Protein; LIP = Lipids; FIB = Fibers; ASH = Ash.
The negative correlation of -0.838 observed between LIP and AMI indicates that as the lipid content in the grains increases, the starch content decreases, and vice versa. This behavior can be attributed to the impact that processing has on the redistribution and transformation of these components within the grain, as the accumulation of lipids in the grains is influenced by factors such as the maturation stage and processing conditions, such as the parboiling time and temperature, which affect both the distribution of starch and lipids (Garutti et al., 2024). Negative correlations were observed between ASH and STA (-0.925) and between STA and MOI (-0.921), suggesting a strong inverse relationship between components, meaning that as one increases, the other tends to decrease, and vice versa.
The positive correlation of 0.864 between MOI and PRO in parboiled rice grains suggests that grains with higher protein content retain more water due to the physicochemical changes caused by the parboiling process. During this process, water is absorbed, and starch gelatinizes, which increases the moisture retention capacity. Denatured proteins form networks that trap water, resulting in a more stable and hydrated structure after drying. This reorganization also improved the interaction between proteins and water, intensifying this radiant atmosphere (Muchlisyiyah et al., 2023).
A positive correlation of intensity 0.750 between ASH and UMI was observed, suggesting that moister grains tend to have more mineral retention capacity, especially after the parboiling process, since it alters the grain structure and facilitates the redistribution of nutrients (Pinta et al., 2024). In Figure 3, the principal components of the proximate composition variables can be analyzed.
Principal components of the variables of the proximate composition for the types of parboiled rice grains.
The Principal Component Analysis (PCA) (Figure 3) shows that CP1 and CP2 together explain 94.6% of the total variability, highlighting the robustness of the method for understanding the data structure, as it meets the minimum limit of 70% (Lu et al., 2024). AMI is more aligned with CP1, indicating that it is a variable that strongly contributes to the first principal component. FIB makes a significant contribution in a direction almost opposite to that of STA, suggesting an inverse relationship. LIP, ASH, UMI, and PRO are closer to each other, indicating that they may have correlations among them.
The Off-Grade grains are associated with higher fiber content (FIB), while the grains of Types 1 and 2 are more associated with starch content (AMI). Types 5 and 3 show greater proximity to protein (PRO) and moisture (UMI), while the grains of Type 4 are related to lipids (LIP) and ash (CIN). During the parboiling process, rice undergoes a heat treatment that promotes the total or partial gelatinization of starch and causes changes in its microstructure, physicochemical properties, and cooking quality (Hu et al., 2019; Feng et al., 2024). For the Off-Grade grains, which generally have higher amounts of bran and husk (where fibers are concentrated), parboiling can intensify fiber retention, reinforcing the association with this component (Huang et al., 2021), as shown in the graph.
The grains of Types 1 and 2, which have higher quality and fewer defects, tend to be richer in starch, and parboiling helps preserve this starchy structure, shifting the relationship of these grains away from fiber (Muchlisyiyah et al., 2023). Meanwhile, Types 5 and 3, which are closer to proteins and moisture, are also impacted by parboiling. The parboiling process promotes hardening and greater preservation of the grain's external structure, which explains the higher fiber retention in Off-Grade grains and the concentration of starch in more refined grains (Ngo et al., 2023).
CONCLUSIONS
Based on the obtained data, it was observed that the grains that compose Type 1 have a lower incidence of defects, suggesting superior quality compared to the other types, as expected from the physical classification. Following this, Type 2 grains stand out primarily due to their high starch content. The grains from Types 3, 4, and 5 demonstrate similar qualities, as they share characteristics such as moisture, protein, lipids, and ash content. Finally, the grains classified as 'Off-Grade' differ from the others due to their high fiber content, possibly intensified by the parboiling process.
These results demonstrate that NIR spectroscopy, combined with multivariate statistical analyses, provides an effective alternative for the physicochemical characterization of parboiled rice. This suggests that, in addition to traditional physical classification, conducting physicochemical analyses may be essential for determining the commercial quality of the grains.
ACKNOWLEDGEMENTS
The authors would like to thank the Federal University of Santa Maria (UFSM), the Graduate Program in Agricultural Engineering (PPGEA), the Postharvest Laboratory (LAPOS), the "Research Group at Postharvest Innovation: Technology, Quality and Sustainability", the Coordination for the Improvement of Higher Education Personnel (CAPES)-Financial Code 001, the National Council for Scientific and Technological Development (CNPq)-Financial Code 304966/2023-1, and the Research Support Foundation of the State of Rio Grande do Sul (FAPERGS)-Financial Code 24/2551-0001150-1, for granting scholarships, financial resources, and physical spaces for the development of the experiments.
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APPENDIX 1
TABLE 1 Pearson correlation values between the proximate composition variables of Types 1 to 5 and Off-type parboiled rice.| Variables | Correlation Value |
|---|---|
| MOI x STA | -0.9219751 |
| MOI x PRO | 0.8649496 |
| MOI x LIP | 0.6405299 |
| MOI x FIB | -0.07711746 |
| MOI x ASH | 0.7507534 |
| STA x PRO | -0.7057491 |
| STA x LIP | -0.8383978 |
| STA x FIB | -0.2947142 |
| STA x ASH | -0.9250413 |
| PRO x LIP | 0.4346841 |
| PRO x FIB | -0.3660012 |
| PRO x ASH | 0.4432174 |
| LIP x FIB | 0.652296 |
| LIP x ASH | 0.9392352 |
| FIB x ASH | 0.5801248 |
