Classification and Authentication of Barley (Hordeum vulgare) Malt Varieties: Combining Attenuated Total Reflectance Mid-infrared Spectroscopy with Chemometrics

Rapid analysis technologies with chemometrics for food authenticity field: A review

In recent years, the problem of food adulteration has become increasingly rampant, seriously hindering the development of food production, consumption, and management. The common analytical methods used to determine food authenticity present challenges, such as complicated analysis processes and time-consuming procedures, necessitating the development of rapid, efficient analysis technology for food authentication. Spectroscopic techniques, ambient ionization mass spectrometry (AIMS), electronic sensors, and DNA-based technology have gradually been applied for food authentication due to advantages such as rapid analysis and simple operation. This paper summarizes the current research on rapid food authenticity analysis technology from three perspectives, including breeds or species determination, quality fraud detection, and geographical origin identification, and introduces chemometrics method adapted to rapid analysis techniques. It aims to promote the development of rapid analysis technology in the food authenticity field.

Multi temporal multispectral UAV remote sensing allows for yield assessment across European wheat varieties already before flowering

High throughput field phenotyping techniques employing multispectral cameras allow extracting a variety of variables and features to predict yield and yield related traits, but little is known about which types of multispectral features are optimal to forecast yield potential in the early growth phase. In this study, we aim to identify multispectral features that are able to accurately predict yield and aid in variety classification at different growth stages throughout the season. Furthermore, we hypothesize that texture features (TFs) are more suitable for variety classification than for yield prediction. Throughout 2021 and 2022, a trial involving 19 and 18 European wheat varieties, respectively, was conducted. Multispectral images, encompassing visible, Red-edge, and near-infrared (NIR) bands, were captured at 19 and 22 time points from tillering to harvest using an unmanned aerial vehicle (UAV) in the first and second year of trial. Subsequently, orthomosaic images were generated, and various features were extracted, including single-band reflectances, vegetation indices (VI), and TFs derived from a gray level correlation matrix (GLCM). The performance of these features in predicting yield and classifying varieties at different growth stages was assessed using random forest models. Measurements during the flowering stage demonstrated superior performance for most features. Specifically, Red reflectance achieved a root mean square error (RMSE) of 52.4 g m-2 in the first year and 64.4 g m-2 in the second year. The NDRE VI yielded the most accurate predictions with an RMSE of 49.1 g m-2 and 60.6 g m-2, respectively. Moreover, TFs such as CONTRAST and DISSIMILARITY displayed the best performance in predicting yield, with RMSE values of 55.5 g m-2 and 66.3 g m-2 across the two years of trial. Combining data from different dates enhanced yield prediction and stabilized predictions across dates. TFs exhibited high accuracy in classifying low and high-yielding varieties. The CORRELATION feature achieved an accuracy of 88% in the first year, while the HOMOGENEITY feature reached 92% accuracy in the second year. This study confirms the hypothesis that TFs are more suitable for variety classification than for yield prediction. The results underscore the potential of TFs derived from multispectral images in early yield prediction and varietal classification, offering insights for HTP and precision agriculture alike.

Advances in infrared spectroscopy and hyperspectral imaging combined with artificial intelligence for the detection of cereals quality

Cereals provide humans with essential nutrients, and its quality assessment has attracted widespread attention. Infrared (IR) spectroscopy (IRS) and hyperspectral imaging (HSI), as powerful nondestructive testing technologies, are widely used in the quality monitoring of food and agricultural products. Artificial intelligence (AI) plays a crucial role in data mining, especially in recent years, a new generation of AI represented by deep learning (DL) has made breakthroughs in analyzing spectral data of food and agricultural products. The combination of IRS/HSI and AI further promotes the development of quality evaluation of cereals. This paper comprehensively reviews the advances of IRS and HSI combined with AI in the detection of cereals quality. The aim is to present a complete review topic as it touches the background knowledge, instrumentation, spectral data processing (including preprocessing, feature extraction and modeling), spectral interpretation, etc. To suit this goal, principles of IRS and HSI, as well as basic concepts related to AI are first introduced, followed by a critical evaluation of representative reports integrating IRS and HSI with AI. Finally, the advantages, challenges and future trends of IRS and HSI combined with AI are further discussed, so as to provide constructive suggestions and guidance for researchers.

A review combining emerging techniques with classical ones for the determination of biscuit quality: advantages and drawbacks

The production of biscuit and biscuit-like products has faced many challenges due to changes in consumer behavior and eating habits. Today's consumer is looking for safe products not only with fresh-like and pleasant taste, but also with long shelf life and health benefits. Therefore, the potentiality of the use of healthier fat and the incorporation of natural antioxidant in the formulation of biscuit has interested, recently, the attention of researchers. The determination of the biscuit quality could be performed by several techniques (e.g., physical, chemical, sensory, calorimetry and chromatography). These classical analyses are unfortunately destructive, expensive, polluting and above all very heavy, to implement when many samples must be prepared to be analyzed. Therefore, there is a need to find fast analytical techniques for the determination of the quality of cereal products like biscuits. Emerging techniques such as near infrared (NIR), mid infrared (MIR) and front face fluorescence spectroscopy (FFFS), coupled with chemometric tools have many potential advantages and are introduced, recently, as promising techniques for the assessment of the biscuit quality.

Identification of Amaranthus Species Using Visible-Near-Infrared (Vis-NIR) Spectroscopy and Machine Learning Methods

The feasibility of rapid and non-destructive classification of six different Amaranthus species was investigated using visible-near-infrared (Vis-NIR) spectra coupled with chemometric approaches. The focus of this research would be to use a handheld spectrometer in the field to classify six Amaranthus sp. in different geographical regions of South Korea. Spectra were obtained from the adaxial side of the leaves at 1.5 nm intervals in the Vis-NIR spectral range between 400 and 1075 nm. The obtained spectra were assessed with four different preprocessing methods in order to detect the optimum preprocessing method with high classification accuracy. Preprocessed spectra of six Amaranthus sp. were used as input for the machine learning-based chemometric analysis. All the classification results were validated using cross-validation to produce robust estimates of classification accuracies. The different combinations of preprocessing and modeling were shown to have a classification accuracy of between 71% and 99.7% after the cross-validation. The combination of Savitzky-Golay preprocessing and Support vector machine showed a maximum mean classification accuracy of 99.7% for the discrimination of Amaranthus sp. Considering the high number of spectra involved in this study, the growth stage of the plants, varying measurement locations, and the scanning position of leaves on the plant are all important. We conclude that Vis-NIR spectroscopy, in combination with appropriate preprocessing and machine learning methods, may be used in the field to effectively classify Amaranthus sp. for the effective management of the weedy species and/or for monitoring their food applications.

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.

Chemical Differentiation of Sugarcane Cultivars Based on Volatile Profile and Chemometric Analysis

Sugarcane (SC) is a perennial grass widely cultivated in tropical and subtropical regions. However, its cultivation in Europe is residual, where Madeira Island, Portugal, is the only region where SC continues to be extensively cultivated. For the first time, the volatile profiles of regional cultivars were established by solid-phase microextraction combined with gas chromatography-mass spectrometry. Different volatile profiles for each cultivar were recognized, identifying 260 volatile organic compounds belonging to 15 chemical classes, such as aldehydes, alcohols, ketones, hydrocarbons, esters, and terpenes. Chemometric analysis procedure, namely, one-way ANOVA with Tukey's test, principal component analysis, partial least-square analysis, linear discriminant analysis, and hierarchical clustering analysis, allowed the differentiation between all regional cultivars. This study represents an important contribution for the maintenance of biodiversity and subsistence of the SC industry in Europe. Furthermore, it is also a valuable contribution to establish the typicality of traditional SC-based products, such as SC honey.

The effect of zinc fertilisation and arbuscular mycorrhizal fungi on grain quality and yield of contrasting barley cultivars

Zinc is essential for the functioning of many enzymes and plant processes and the malting process. Arbuscular mycorrhizal fungi (AMF) can improve zinc (Zn) uptake in the important cereal crop barley (Hordeum vulgare) on Zn-deficient soils. Here we investigated the impacts of Zn fertilisation and AMF on the yield and grain quality of malting barley cultivars. Five barley genotypes were inoculated or not with the AMF Rhizophagus irregularis, and grown in pots either fertilised with Zn or not. Measurements of Zn nutrition and yield were made for all cultivars. Further analyses of grain biochemical composition, including starch, β-glucan and arabinoxylan contents, and analysis of ATR-MIR spectra were made in two contrasting cultivars. Mycorrhizal colonisation generally resulted in decreased biomass, but increased grain dimensions and mean grain weight. Barley grain yield and biochemical qualities were highly variable between cultivars, and the ATR-MIR spectra revealed grain compositional differences between cultivars and AMF treatments. Mycorrhizal fungi can affect barley grain Zn concentration and starch content, but grain biochemical traits including β-glucan and arabinoxylan contents were more conserved by the cultivar, and unaffected by AMF inoculation. The ATR-MIR spectra revealed that there are other grain characteristics affected by AMF that remain to be elucidated.

On-The-Go Hyperspectral Imaging Under Field Conditions and Machine Learning for the Classification of Grapevine Varieties

Grapevine varietal classification is an important plant phenotyping issue for grape growing and wine industry. This task has been achieved from destructive techniques like classic ampelography and DNA analysis under laboratory conditions. This work displays a new approach for the classification of a high number of grapevine (Vitis vinifera L.) varieties under field conditions using on-the-go hyperspectral imaging and different machine learning algorithms. On-the-go imaging was performed under natural illumination using a hyperspectral camera mounted on an all-terrain vehicle at 5 km/h. Spectra were acquired over two different leaf phenological stages on the canopy of 30 different varieties on a commercial vineyard located in La Rioja, Spain. A total of 1,200 spectral samples were generated. Support vector machines (SVM) and artificial neural networks (multilayer perceptrons, MLP) were used for the development of a large number of models, testing different algorithm parameters and spectral pre-processing techniques. Both classifiers yielded notable performance values and were able to train models with recall F1 scores and area under the receiver operating characteristic curve marks up to 0.99 for 5-fold cross validation. Statistical analyses supported that the best SVM kernel was linear and the best activation function for MLP was the hyperbolic tangent function. The prediction performance for individual varieties of MLP ranged from 0.94 to 0.99, displaying low levels of variability. In the case of SVM, slightly higher differences were obtained, ranging from 0.83 to 0.97 for individual varieties. These results support the possibility of deploying an on-the-go hyperspectral imaging system in the field capable of successfully classifying leaves from different grapevine varieties. This technology could thus be considered as a new useful non-destructive tool for plant phenotyping under field conditions.

Assessing Wheat Traits by Spectral Reflectance: Do We Really Need to Focus on Predicted Trait-Values or Directly Identify the Elite Genotypes Group?

Phenotyping, via remote and proximal sensing techniques, of the agronomic and physiological traits associated with yield potential and drought adaptation could contribute to improvements in breeding programs. In the present study, 384 genotypes of wheat (Triticum aestivum L.) were tested under fully irrigated (FI) and water stress (WS) conditions. The following traits were evaluated and assessed via spectral reflectance: Grain yield (GY), spikes per square meter (SM2), kernels per spike (KPS), thousand-kernel weight (TKW), chlorophyll content (SPAD), stem water soluble carbohydrate concentration and content (WSC and WSCC, respectively), carbon isotope discrimination (Δ13C), and leaf area index (LAI). The performances of spectral reflectance indices (SRIs), four regression algorithms (PCR, PLSR, ridge regression RR, and SVR), and three classification methods (PCA-LDA, PLS-DA, and kNN) were evaluated for the prediction of each trait. For the classification approaches, two classes were established for each trait: The lower 80% of the trait variability range (Class 1) and the remaining 20% (Class 2 or elite genotypes). Both the SRIs and regression methods performed better when data from FI and WS were combined. The traits that were best estimated by SRIs and regression methods were GY and Δ13C. For most traits and conditions, the estimations provided by RR and SVR were the same, or better than, those provided by the SRIs. PLS-DA showed the best performance among the categorical methods and, unlike the SRI and regression models, most traits were relatively well-classified within a specific hydric condition (FI or WS), proving that classification approach is an effective tool to be explored in future studies related to genotype selection.