Sensory Quality Evaluation of Rice Using Visible and Shortwave Near-Infrared Spectroscopy

Geographical origin identification of Khao Dawk Mali 105 rice using combination of FT-NIR spectroscopy and machine learning algorithms

The mislabelled Khao Dawk Mali 105 rice coming from other geographical region outside the Thung Kula Rong Hai region is extremely profitable and difficult to detect; to prevent retail fraud (that adversely affects both the food industry and consumers), it is vital to identify geographical origin. Near infrared spectroscopy can be used to detect the specific content of organic moieties in agricultural and food products. The present study implemented the combinatorial method of FT-NIR spectroscopy with chemometrics to identify geographical origin of Khao Dawk Mali 105 rice. Rice samples were collected from 2 different region including the north and northeast of Thailand. NIR spectra data were collected in range of 12,500 - 4,000 cm-1 (800-2,500 nm). Five machine learning algorithms including linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA), C-support vector classification (C-SVC), backpropagation neural networks (BPNN), hybrid principal component analysis-neural network (PC-NN) and K-nearest neighbors (KNN) were employed to classify NIR data of rice samples with full wavelength and selected wavelength by Extremely Randomized Trees (Extra trees) algorithm. Based on the findings, geographical origin of rice could be specified quickly, cheaply, and reliably using combination of NIRS and machine learning. All models creating by full wavelength and selected wavelength exhibited accuracy between 65 and 100 % for identifying geographical region of rice. It was proven that NIR spectroscopy may be used for the quick and non-destructive identification of geographical origin of Khao Dawk Mali 105 rice.

Terahertz reconfigurable metasensor for specific recognition multiple and mixed chemical substances based on AIT fingerprint enhancement

Terahertz (THz) fingerprint metasensing is an effective method to identify chemical substances in a rapid and non-destructive way. Currently, two main principles are used in THz metasensing: the change of the real part of permittivity causing the dip resonance frequency deviation, and the fingerprint peak of the imaginary part of permittivity causing the dip resonance splitting (absorption induced transparency, AIT). Most previous work investigated AIT detection for only single chemical substance. The suitable AIT metasensor structure are still required for simultaneously measurement of multiple and mixture chemical substances. In this manuscript, we proposed the N-order concentric rings metasensor for specific recognition multiple and mixed chemical substances based on AIT fingerprint enhancement. The structure has broadband multiple plasmonic resonance dips which are generated by near field dipole resonances. The equivalent circuit model was built to realize the reconfigurable function. Then, 5-order concentric rings structure was designed and fabricated for simultaneously specific recognition of four chemical substances (α-lactose, benzoic acid, vitamin B2 and 2, 5-dichloroanline). The influence of the real and imaginary part of the chemical substances' permittivity on AIT effect had discussed in details. Simulation results indicated that the frequency-deviation of the resonance dip can be stabilized and will not be changed when the concentration of chemical substances is over 20 mg/mL. As shifted plasmonic resonance peaks match the chemical substances' imaginary part of permittivity fingerprint spectra, the perfect AIT effect can be realized. The metasensor can simultaneously and non-destructively conduct a specific detection of α-lactose, benzoic acid, vitamin B2 and 2,5-dichloroanline, and their mixture. The limit of detections of α-lactose, benzoic acid, vitamin B2 and 2,5-dichloroanline are 8.61 mg/mL, 6.96 mg/mL, 7.54 mg/mL and 8.35 mg/mL, respectively. Also, the sensitivity of the metasensor can reach 0.00211, 0.00208, 0.00211 and 0.00219 (unit: 1/mg/mL), respectively. By utilizing one-way analysis of variance method, the possibility of recognition error for each chemical substance is lower than 0.001. Our metasensor provides a novel and accurate platform for THz fingerprint sensing.

Perspectives on Novel Technologies of Processing and Monitoring the Safety and Quality of Prepared Food Products

With the changes of lifestyles and rapid growth of prepared food industry, prepared fried rice that meets the consumption patterns of contemporary young people has become popular in China. Although prepared fried rice is convenient and nutritious, it has the following concerns in the supply chain: (1) susceptible to contamination by microorganisms; (2) rich in starch and prone to stall; and (3) vegetables in the ingredients have the issues of water loss and discoloration, and meat substances are vulnerable to oxidation and deterioration. As different ingredients are used in prepared fried rice, their food processing and quality monitoring techniques are also different. This paper reviews the key factors that cause changes in the quality of prepared fried rice, and the advantages and limitations of technologies in the processing and monitoring processes. The processing technologies for prepared fried rice include irradiation, high-voltage electric field, microwave, radio frequency, and ohmic heating, while the quality monitoring technologies include Raman spectral imaging, near-infrared spectral imaging, and low-field nuclear magnetic resonance technology. These technologies will serve as the foundation for enhancing the quality and safety of prepared fried rice and are essential to the further development of prepared fried rice in the emerging market.

Accurate prediction of the eating and cooking quality of rice using artificial neural networks and the texture properties of cooked rice

Accurate prediction of the eating and cooking quality (ECQ) of rice is of great importance. Statistical and machine learning models were developed to predict the overall acceptability of cooked rice. The results showed that the models developed using stepwise multiple linear regression, principal component analysis plus multiple linear regression, partial least square regression, k-nearest neighbor, random forest, and gradient boosted decision tree had determination coefficients (R²) of 0.156-0.452, 0.357, 0.160-0.460, 0.192-0.746, 0.453-0.708, and 0.469-0.880, respectively, which were improved to 0.675-0.979 by artificial neural networks (ANN) models. The ANN models also had lower root mean square errors (0.574-1.32). Further, the ANN model using textural properties could accurately predict 92.1 % of overall acceptability, which could be improved to >96 % using the components and/or pasting characteristics. Overall, the accuracy of ECQ prediction was substantially improved by the model developed using ANN with texture properties of rice.

Volatile Organic Compounds, Evaluation Methods and Processing Properties for Cooked Rice Flavor

Rice (Oryza sativa L.), as the main refined grain in China, has attracted much attention in terms of quality. Rice is usually consumed after cooking, and it is a commonly staple food. Nowdays, people's requirements for cooked rice focus more on the taste characteristics and quality. Furthermore, aroma is one of the primary sensory reference points, which is the most intuitive way for people to judge cooked rice. By integrating and analyzing the researches of cooked rice aroma identification in recent five years, this paper expounds the extraction and identification methods (sensory evaluation method, GC-MS, SPME, MOS sensors, electronic nose, etc.) of the flavor substances in cooked rice, as the processing methods and properties of cooked rice, and the volatile organic compounds of cooked rice under different conditions are summarized as well.

Chestnut Cultivar Identification through the Data Fusion of Sensory Quality and FT-NIR Spectral Data

The world production of chestnuts has significantly grown in recent decades. Consumer attitudes, increasingly turned towards healthy foods, show a greater interest in chestnuts due to their health benefits. Consequently, it is important to develop reliable methods for the selection of high-quality products, both from a qualitative and sensory point of view. In this study, Castanea spp. fruits from Italy, namely Sweet chestnut cultivar and the Marrone cultivar, were evaluated by an official panel, and the responses for sensory attributes were used to verify the correlation to the near-infrared spectra. Data fusion strategies have been applied to take advantage of the synergistic effect of the information obtained from NIR and sensory analysis. Large nuts, easy pellicle removal, chestnut aroma, and aromatic intensity render Marrone cv fruits suitable for both the fresh market and candying, i.e., marron glacé. Whereas, sweet chestnut samples, due to their characteristics, have the potential to be used for secondary food products, such as jam, mash chestnut, and flour. The research lays the foundations for a superior data fusion approach for chestnut identification in terms of classification sensitivity and specificity, in which sensory and spectral approaches compensate each other's drawbacks, synergistically contributing to an excellent result.

Assessment and Feasibility Study of Lemon Ripening Using X-ray Image of Information Visualization

Digital radiography (DR) is a mature technology and has been broadly used in medical diagnosis. Currently, it’s also used for fruit quality inspection in the market. This purpose of the study is to conduct non-destructive experiments for visual comparisons of digital radiography images, further construct visualized grayscale image analysis technology, and analyze the changes in lemon quality and ripening using quantitative statistical methods. The materials used for the experiments were three lemons of different ripening. A general medical X-ray DR system for was used in this study for 2D digital radiography. The medical X-ray DR images were created based on the Digital Imaging and Communications in Medicine (DICOM) standard. Photometric interpretation of monochrome was applied to create multi-layered grayscale images. Then quantitative analyses and comparisons were performed with image matrix structures and grayscale pixel values in the tissues using visualization techniques and statistical methods. After layer segmentation on the radiological images, the correlations between the lemon structures and tissue changes were assessed by using the Kruskal–Wallis test. The results showed that the p values for lemon, fiber, and pulp were all under 0.05, while the peel layer did not exhibit significant change. The pulp layer is the best region for statistical analyses to determine the lemon ripening. In conclusion, this study can provide a solid reference for future quality classification in the agricultural market. The research findings can be referenced for developing computing techniques applied to agricultural inspection, expanding the scope of application of the medical DR technology.

Estimating the sensory qualities of tomatoes using visible and near-infrared spectroscopy and interpretation based on gas chromatography-mass spectrometry metabolomics

The ability to estimate the sensory quality of intact tomatoes rapidly and non-destructively using visible and near-infrared spectroscopy (Vis-NIRS) is important for the tomato industry. In this study, a combination of partial least squares regression (PLSR) analysis and the stepwise selectivity ratio (SWSR) method was used to study the ability of Vis-NIRS to predict 19 sensory attributes in intact tomatoes. The PLSR models constructed based on the informative wavelengths selected by the SWSR method predicted 8 sensory attributes well, particularly the sweetness attribute (correlation coefficient of validation of 0.92). Moreover, based on the tomato metabolites determined by GC-MS analysis, high intercorrelations between sensory attributes, metabolites, and the selected informative wavelengths were found through principal component analysis, as well as the high correlation coefficients between them. The results confirm the feasibility and reliability of Vis-NIRS and the informative wavelengths selected by SWSR to predict the sensory quality of whole tomatoes.

Simultaneous Determination of Several Fiber Contents in Blended Fabrics by Near-Infrared Spectroscopy and Multivariate Calibration

The qualitative and quantitative determination of the components of textile fibers takes an important position in quality control. A fast and nondestructive method of simultaneously analyzing four fiber components in blended fabrics was studied by near-infrared (NIR) spectroscopy combined with multivariate calibration. Two sample sets including 39 and 25 samples were designed by simplex mixture lattice design methods and used for experiment. Four components include wool, polyester, polyacrylonitrile, and nylon and their mixture is one of the most popular formulas of textiles. Uninformative variable elimination-partial least squares (UVEPLS) and the full-spectrum partial least squares (PLS) were used as the tool. On the test set, the mean standard error of prediction (SEP) and the mean ratio of the standard deviation of the response variable and SEP (RPD) of the full-spectrum PLS model and UVEPLS model were 0.38, 0.32 and 7.6, 8.3, respectively. This result reveals that the UVEPLS can construct local models with acceptable and better performance than the full-spectrum PLS. It indicates that this method is valuable for nondestructive analysis in the field of wool content detection since it can avoid time-consuming, costly, and laborious wet chemical analysis.

Improving Rice Grain Quality: State-of-the-Art and Future Prospects

Rice grain quality encompasses complex interrelated traits that cover biochemical composition, cooking, eating, nutritional, and sensory properties. Because rice endosperm is composed mainly of starch, rice grain quality is traditionally defined by characterizing starch structure and composition, which is then subsequently correlated with functional properties of the grain. The current proxy tests routinely used to describe rice grain quality preferences are rather limited to the estimation of apparent amylose content, gelatinization temperature, and gel consistency. Additional tests that characterize starch property, viscoelasticity, grain texture, and aroma are also employed in more advanced laboratories. However, these tests are not routinely applied in breeding programs to distinguish cooking quality classes to reflect evolving consumer preference and market demand. As consumer preferences in Asia and all over the world are diverse due to varied demographics and culture, defining uniform attributes to capture regional grain quality preferences becomes more challenging. Hence, novel and innovative proxy tests are needed to characterize rice grain quality to meet the demand for consumer preferences of commercially-released cultivars. In this chapter, the current methods employed in rice grain quality monitoring are succinctly reviewed. Future prospects for improvement are identified, introducing cutting edge technologies that can facilitate high-throughput screening of rice diversity panels and breeding lines. Aside from addressing the requirements for quality improvement in the traditional inbred rice breeding programs, we also tackled the need to enhance grain quality in the hybrid rice sector.