A comparison between NIR and ATR-FTIR spectroscopy for varietal differentiation of Spanish intact almonds

NIR Sensing Technologies for the Detection of Fraud in Nuts and Nut Products: A Review

Food fraud is a major threat to the integrity of the nut supply chain. Strategies using a wide range of analytical techniques have been developed over the past few years to detect fraud and to assure the quality, safety, and authenticity of nut products. However, most of these techniques present the limitations of being slow and destructive and entailing a high cost per analysis. Nevertheless, near-infrared (NIR) spectroscopy and NIR imaging techniques represent a suitable non-destructive alternative to prevent fraud in the nut industry with the advantages of a high throughput and low cost per analysis. This review collects and includes all major findings of all of the published studies focused on the application of NIR spectroscopy and NIR imaging technologies to detect fraud in the nut supply chain from 2018 onwards. The results suggest that NIR spectroscopy and NIR imaging are suitable technologies to detect the main types of fraud in nuts.

Mid-Level Data Fusion Combined with the Fingerprint Region for Classification DON Levels Defect of Fusarium Head Blight Wheat

In this study, a method of mid-level data fusion with the fingerprint region was proposed, which was combined with the characteristic wavelengths that contain fingerprint information in NIR and FT-MIR spectra to detect the DON level in FHB wheat during wheat processing. NIR and FT-MIR raw spectroscopy data on normal wheat and FHB wheat were obtained in the experiment. MSC was used for pretreatment, and characteristic wavelengths were extracted by CARS, MGS and XLW. The variables that can effectively reflect fingerprint information were retained to build the mid-level data fusion matrix. LS-SVM and PLS-DA were applied to investigate the performance of the single spectroscopic model, mid-level data fusion model and mid-level data fusion with fingerprint information model, respectively. The experimental results show that mid-level data fusion with a fingerprint information strategy based on fused NIR and FT-MIR spectra represents an effective method for the classification of DON levels in FHB wheat samples.

Amazonian cacao-clone nibs discrimination using NIR spectroscopy coupled to naïve Bayes classifier and a new waveband selection approach

Near-Infrared Spectroscopy (NIRS) has shown to be helpful in the study of rice, tea, cocoa, and other foods due to its versatility and reduced sample treatment. However, the high complexity of the data produced by NIR sensors makes necessary pre-treatments such as feature selection techniques that produce compact profiles. Supervised and unsupervised techniques have been tested, creating different subsets of features for classification, which affect the performance of the classifiers based on such compact profiles. In this sense, we propose and test a new covering array feature selection (CAFS) algorithm coupled to the naïve Bayes classifier (NBC) to discriminate among Amazonian cacao nibs from six cacao clones. The CAFS wrapper approach looks for the wavebands that maximize the F₁-score, and then, are more relevant for classification. For this purpose, cacao pods of six varieties were collected, and their grains were extracted and processed (fermented, dried, roasted, and milled) to obtain cacao nibs. Then from each clone NIR spectral profiles in the range of 1100-2500 nm were extracted, and relevant wavebands were selected using the proposed CAFS algorithm. For comparison, two standard feature selection techniques were implemented the multi-cluster feature selection MCFS and the eigenvector centrality feature selection ECFS. Then, based on the different selected variables, three NBCs were built and compared among them through statistical metrics. The results showed that using the wavebands selected by CAFS, the NBC performed an average accuracy of 99.63%; being this superior to the 94.92% and 95.79% for ECFS and MCFS respectively. These results showed that the wavebands selected by the proposed CAFS algorithm allowed obtaining a better fit concerning other feature selection methods reported in the literature.

Fabrication and Characterization of Composite Biofilm of Konjac Glucomannan/Sodium Lignosulfonate/ε-Polylysine with Reinforced Mechanical Strength and Antibacterial Ability

In order to enforce the mechanical strength and antibacterial ability of biofilm and explore the underlying mechanism, sodium lignosulfonate (SL) and ε-polylysine (ε-PL) were introduced to fabricate the composite film of konjac glucomannan (KGM)/SL/ε-PL in the present study. According to our previous method, 1% (w/v) of KGM was the optimal concentration for the film preparation method, on the basis of which the amount of SL and ε-PL were screened by mechanical properties enforcement of film. The structure, mechanical performance and thermal stability of the film were characterized by SEM, FTIR, TGA and tensile strength tests. The optimized composite film was comprised of KGM 1% (w/v), SL 0.2% (w/v), and ε-PL 0.375% (w/v). The tensile strength (105.97 ± 4.58 MPa, p < 0.05) and elongation at break (95.71 ± 5.02%, p < 0.05) of the KGM/SL/ε-PL composite film was greatly improved compared with that of KGM. Meanwhile, the thermal stability and antibacterial property of film were also enhanced by the presence of SL and ε-PL. In co-culturation mode, the KGM/SL/ε-PL composite film showed good inhibitory effect on Escherichia coli (22.50 ± 0.31 mm, p < 0.05) and Staphylococcus aureus (19.69 ± 0.36 mm, p < 0.05) by determining the inhibition zone diameter. It was revealed that KGM/SL/ε-PL composite film shows enhanced mechanical strength and reliable antibacterial activities and it could be a potential candidate in the field of food packaging.

Application of Visible/Infrared Spectroscopy and Hyperspectral Imaging With Machine Learning Techniques for Identifying Food Varieties and Geographical Origins

Food quality and safety are strongly related to human health. Food quality varies with variety and geographical origin, and food fraud is becoming a threat to domestic and global markets. Visible/infrared spectroscopy and hyperspectral imaging techniques, as rapid and non-destructive analytical methods, have been widely utilized to trace food varieties and geographical origins. In this review, we outline recent research progress on identifying food varieties and geographical origins using visible/infrared spectroscopy and hyperspectral imaging with the help of machine learning techniques. The applications of visible, near-infrared, and mid-infrared spectroscopy as well as hyperspectral imaging techniques on crop food, beverage, fruits, nuts, meat, oil, and some other kinds of food are reviewed. Furthermore, existing challenges and prospects are discussed. In general, the existing machine learning techniques contribute to satisfactory classification results. Follow-up researches of food varieties and geographical origins traceability and development of real-time detection equipment are still in demand.

Geographical discrimination of Chinese winter wheat using volatile compound analysis by HS-SPME/GC-MS coupled with multivariate statistical analysis

This study aimed to develop a potential analytical method to discriminate the Chinese winter wheat according to geographical origin and cultivars. A total of 90 wheat samples of 10 different wheat cultivars among three regions were examined by headspace solid phase microextraction coupled with gas chromatography-mass spectrometry (GC-MS). The peak areas of 32 main volatile compounds were selected and subjected to statistical analysis, which revealed significant differences among different regions and cultivars. Multivariate analysis of variance showed a significant influence of regions, wheat genotypes, and their interaction on the volatile composition of wheat. Principal component analysis of the aromatic profile showed better visualization for wheat geographical origins. Finally, a classification model based on the linear discriminant analysis was successfully constructed for the discrimination of regions and cultivars with the correct classification percentages of 90 and 100%, respectively.