Authentication of Rice (Oryza sativa L.) Using Near Infrared Spectroscopy Combined with Different Chemometric Classification Strategies

Geographical discrimination of Paw San rice cultivated in different regions of Myanmar using near-infrared spectroscopy, headspace-gas chromatography-ion mobility spectrometry and chemometrics

Paw San rice, also known as "Myanmar pearl rice", is considered the highest quality rice in Myanmar. There are considerable differences in terms of the premium commercial value of Paw San rice, which is an incentive for fraud, e.g. adulteration with cheaper rice varieties or mislabelling its geographical origin. Shwe Bo District is one of the most popular rice growing areas in the Sagaing region of Myanmar which produces the most valued and highly priced Paw San rice (Shwe Bo Paw San). The verification of the geographical origin of Paw San rice is not readily undertaken in the rice supply chain because the existing analytical approaches are time-consuming and expensive. Therefore, there is a need for rapid, robust and cost-effective analytical techniques for monitoring the authenticity and geographical origin of Paw San rice. In this 4-year study, two rapid screening techniques, Fourier-transform near-infrared (FT-NIR) spectroscopy and headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS), coupled with chemometric modelling, were applied and compared for the regional differentiation of Paw San rice. In addition, low-level fusion of the FT-NIR and HS-GC-IMS data was performed and its effect on the discriminative power of the chemometric models was assessed. Extensive model validation, including the validation using independent samples from a different production year, was performed. Furthermore, the effect of the sample preparation technique (grinding versus no sample preparation) on the performance of the discriminative model, obtained with FT-NIR spectral data, was assessed. The study discusses the suitability of FT-NIR spectroscopy, HS-GC-IMS and the combination of both approaches for rapid determination of the geographical origin of Paw San rice. The results demonstrated the excellent potential of the FT-NIR spectroscopy as well as HS-GC-IMS for the differentiation of Paw San rice cultivated in two distinct geographical regions. The OPLS-DA model, built using FT-NIR data of rice from 3 production years, achieved 96.67% total correct classification rate of an independent dataset from the 4th production year. The DD-SIMCA model, built using FT-NIR data of ground rice, also demonstrated the highest performance: 94% sensitivity and 97% specificity. This study has demonstrated that FT-NIR spectroscopy can be used as an accessible, rapid and cost-effective screening tool to discriminate between Paw San rice cultivated in the Shwe Bo and Ayeyarwady regions of Myanmar.

Deciphering the Microbiological Mechanisms Underlying the Impact of Different Storage Conditions on Rice Grain Quality

Different storage conditions can influence microbial community structure and metabolic functions, affecting rice grains' quality. However, the microbiological mechanisms by which different storage conditions affect the quality of rice grains are not yet well understood. This study monitored the quality (the content of starch, protein, etc.) and microbial community structure of rice grains stored under different storage conditions with nitrogen gas atmosphere (RA: normal temperature, horizontal ventilation, RB: normal temperature, vertical ventilation, RC: quasi-low temperature, horizontal ventilation). The results revealed that the rice grains stored under condition RB exhibited significantly lower quality compared to condition RA and RC. In addition, under this condition, the highest relative abundance of Aspergillus (16.0%) and Penicillium (0.4%) and the highest levels of aflatoxin A (3.77 ± 0.07 μg/kg) and ochratoxin B1 (3.19 ± 0.05 μg/kg) were detected, which suggested a higher risk of fungal toxin contamination. Finally, co-occurrence network analysis was performed, and the results revealed that butyl 1,2-benzenedicarboxylate was negatively correlated (p < 0.05) with Moesziomyces and Alternaria. These findings will contribute to the knowledge base of rice storage management and guide the development of effective control measures against undesirable microbial activities.

Differentiation of Transylvanian fruit distillates using supervised statistical tools based on isotopic and elemental fingerprint

BACKGROUND

The spirit drinks industry is one of the largest in the world. Fruit distillates require adequate analysis methods combined with statistical tools to build differentiation models, according to distinct criteria (geographical and botanical origin, producer's fingerprint, respectively). Over time a database of alcoholic beverage fingerprints can be generated, being very important for product safety and authenticity control.

RESULTS

To control the distillates' geographical origin, linear discriminant analysis (LDA) revealed that the cross-validation classification was correct for 88.2% of samples, but partial least squares discriminant analysis (PLS-DA) was slightly better suited for this purpose, with a correct classification rate of 91.2%. LDA effectiveness was proven for the trademark fingerprint differentiation, which was achieved at 93.5%, compared to 89.1% for PLS-DA. The principal predictors obtained by LDA were the same both for geographical origin and producer differentiation: B, δ¹³ C, Na, Cu, Ca and Be; highlighting the fact that in the production process of distillates each producer used fruits coming from the respective specific region. Through PLS-DA, some of the discrimination markers were the same for geographical origin and producer's identification, but others were completely specific: the rare earth elements Eu and Er only for geographical origin differentiation, and Cu solely as predictor for producer's identification. Regarding distillates' fruit variety, the correct discrimination rates of plum spirits from the rest were 84.2% for PLS-DA and 63% for LDA.

CONCLUSION

LDA and PLS-DA were suitable for differentiation models development of fruits spirits according to geographical region, producer and fruit variety based on isotopic and elemental fingerprint. © 2022 Society of Chemical Industry.

Identification of aged-rice adulteration based on near-infrared spectroscopy combined with partial least squares regression and characteristic wavelength variables

The long-term storage of rice will inevitably be involved in the deterioration of edible quality, and aged rice poses a great threat to food safety and human health. The acid value can be employed as a sensitive index for the determination of rice quality and freshness. In this study, near-infrared spectra of three kinds of rice (Chinese Daohuaxiang, southern japonica rice, and late japonica rice) mixed with different proportions of aged rice were collected. The partial least squares regression (PLSR) model with different preprocessing was constructed to identify the aged rice adulteration. Meanwhile, a competitive adaptive reweighted sampling (CARS) algorithm was used to extract the optimization model of characteristic variables. The constructed CARS-PLSR model method could not only reduce greatly the number of characteristic variables required by the spectrum but also improve the identification accuracy of three kinds of aged-rice adulteration. As above, this study proposed a rapid, simple, and accurate detection method for aged-rice adulteration, providing new clues and alternatives for the quality control of commercial rice.

Geographical Origin Differentiation of Rice by LC–MS-Based Non-Targeted Metabolomics

Many factors, such as soil, climate, and water source in the planting area, can affect rice taste and quality. Adulterated rice is common in the market, which seriously damages the production and sales of high-quality rice. Traceability analysis of rice has become one of the important research fields of food safety management. In this study, LC–MS-based non-targeted metabolomics technology was used to trace four rice samples from Heilongjiang and Jiangsu Provinces, namely, Daohuaxiang (DH), Huaidao No. 5 (HD), Songjing (SJ), and Changlixiang (CL). Results showed that the discrimination accuracy of the partial least squares discriminant analysis (PLS-DA) model was as high as 100% with satisfactory prediction ability. A total of 328 differential metabolites were screened, indicating significant differences in rice metabolites from different origins. Pathway enrichment analysis was carried out on the four rice samples based on the KEGG database to determine the three metabolic pathways with the highest enrichment degree. The main biochemical metabolic pathways and signal transduction pathways involved in differential metabolites in rice were obtained. This study provides theoretical support for the geographical origins of rice and elucidates the change mechanism of rice metabolic pathways, which can shed light on improving rice quality control.

DNA-Based Tools to Certify Authenticity of Rice Varieties—An Overview

Rice (Oryza sativa L.) is one of the most cultivated and consumed crops worldwide. It is mainly produced in Asia but, due to its large genetic pool, it has expanded to several ecosystems, latitudes and climatic conditions. Europe is a rice producing region, especially in the Mediterranean countries, that grow mostly typical japonica varieties. The European consumer interest in rice has increased over the last decades towards more exotic types, often more expensive (e.g., aromatic rice) and Europe is a net importer of this commodity. This has increased food fraud opportunities in the rice supply chain, which may deliver mixtures with lower quality rice, a problem that is now global. The development of tools to clearly identify undesirable mixtures thus became urgent. Among the various tools available, DNA-based markers are considered particularly reliable and stable for discrimination of rice varieties. This review covers aspects ranging from rice diversity and fraud issues to the DNA-based methods used to distinguish varieties and detect unwanted mixtures. Although not exhaustive, the review covers the diversity of strategies and ongoing improvements already tested, highlighting important advantages and disadvantages in terms of costs, reliability, labor-effort and potential scalability for routine fraud detection.

Water Spectral Patterns Reveals Similarities and Differences in Rice Germination and Induced Degenerated Callus Development

In vivo monitoring of rice (Oryza sativa L.) seed germination and seedling growth under general conditions in closed Petri dishes containing agar base medium at room temperature (temperature = 24.5 ± 1 °C, relative humidity = 76 ± 7% (average ± standard deviation)), and induced degenerated callus formation with plant growth regulator, were performed using short-wavelength near-infrared spectroscopy and aquaphotomics over A period of 26 days. The results of spectral analysis suggest changes in water absorbances due to the production of common metabolites, as well as increases in biomass and the sizes of the samples. Quantitative models built to predict the day of the development provided better accuracy for rice seedlings growth compared to callus formation. Eight common water bands were identified as presenting prominent changes in the absorbance pattern. The water matrix of only rice seedlings showed three developmental stages: firstly expressing a predominantly weakly hydrogen-bonded state, then a more strongly hydrogen-bonded state, and then, again, a weakly hydrogen-bonded state at the end. In rice callus induction and proliferation, no similar change in water absorbance pattern was observed. The presented findings indicate the potential of aquaphotomics for the in vivo detection of degeneration in cell development.

Geographical Differentiation of Hom Mali Rice Cultivated in Different Regions of Thailand Using FTIR-ATR and NIR Spectroscopy

Although Hom Mali rice is considered the highest quality rice in Thailand, it is susceptible to adulteration and substitution. There is a need for rapid, low-cost and efficient analytical techniques for monitoring the authenticity and geographical origin of Thai Hom Mali rice. In this study, two infrared spectroscopy techniques, Fourier-transform infrared spectroscopy with attenuated total reflection (FTIR-ATR) and near-infrared (NIR) spectroscopy, were applied and compared for the differentiation of Thai Hom Mali rice from two geographical regions over two production years. The Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) model, built using spectral data from the benchtop FTIR-ATR, achieved 96.97% and 100% correct classification of the test dataset for each of the production years, respectively. The OPLS-DA model, built using spectral data from the portable handheld NIR, achieved 84.85% and 86.96% correct classification of the test dataset for each of the production years, respectively. Direct NIR analysis of the polished rice grains (i.e., no sample preparation) was determined as reliable for analysis of ground rice samples. FTIR-ATR and NIR spectroscopic analysis both have significant potential as screening tools for the rapid detection of fraud issues related to the geographical origin of Thai Hom Mali rice.