Classification of Grain Maize (Zea mays L.) from Different Geographical Origins with FTIR Spectroscopy—a Suitable Analytical Tool for Feed Authentication?

Remediation of arsenic-contaminated calcareous agricultural soils by iron-oxidizing bacteria combined with organic fertilizer

In arid soil with low-iron and high-calcium carbonate contents, the fate of arsenic (As) is mainly controlled by the contents of calcium and organic matter in the soil. However, there is still a lack of knowledge about their interaction and that effect on their absorption by maize. The purpose of this study was to explore the long-term immobilization and repair mechanism of in situ As-contaminated farmland. We designed three treatments: iron-oxidizing bacteria (FeOB), organic fertilizer, FeOB and organic fertilizer added in combination. After 140-day field farmland remediation trial, the results showed that the FeOB can effectively immobilize the water-soluble As (F_S1) in soil, and the organic fertilizer promoted the remediation of FeOB. In addition, the content of As in maize grains was reduced after treatment by FeOB and organic fertilizer. The XRD and XPS analysis of the topsoil showed that the combined treatment of FeOB and organic fertilizer promoted the formation of calcium arsenate mineral with low solubility and high stability; As(III) would gradually transform into As(V). The biological iron (hydr)oxide can increase the contents of Fe and As in the rhizosphere and form iron plaques on the surface of the roots by SEM-EDS analysis of maize root. Collectively, these results clarify the main biogeochemical ways to control the fate of As in calcareous soils with low-iron and low-organic matter contents and provide a basis for in situ remediation of As.

A comparative evaluation of the effects of seed invigoration treatments with precursor zinc salt and nano-sized zinc oxide (ZnO) particles on vegetative growth, grain yield, and quality characteristics of Zea mays

Introduction

The zinc micronutrient fertilizers have a critical impact on the grain productivity and quality attributes of maize. However, the low use-efficiency issues of the applied Zn-fertilizers are required to be addressed through the development of novel formulations and alternative application techniques.

Objectives

This field study investigates the comparative impact of seed invigoration (including seed priming and coating) treatments with bulk zinc and ZnO nanoparticles (ZnONPs).

Methods

The two seed treatments with two different zinc sources at three different concentrations of 0, 20, and 40 mg L−1 each, for a total of ten treatments, were evaluated for vegetative growth, photosynthetic pigments, grain yield, and quality traits in Zea mays.

Results

The total chlorophyll content was improved by ZnONPs seed priming at the V8 stage. However, there were plants that grew tall, bearing longer ears with bulk ZnSO4 and the untreated control. Yield-contributing factors like number of seeds per cob, and 1000-grain weight were marginally improved by ZnONPs treatment. Overall, only cob weight, starch, total soluble protein, and soil nutrient (N, P, K, and Zn) content were significantly enhanced by ZnONPs treatment. Furthermore, no negative effect was recorded on the soil microbiological and enzymatic activities seed treatment with both the zinc sources.

Conclusion

The seed treatment, i.e. coating and priming with ZnONPs, did not significantly alter the grain yield, but the seed starch and total soluble protein content were improved.

Performance Evaluation of Focal Plane Array (FPA)-FTIR and Synchrotron Radiation (SR)-FTIR Microspectroscopy to Classify Rice Components

The development of biochemical analysis techniques to study heterogeneous biological samples is increasing. These techniques include synchrotron radiation Fourier transform infrared (SR-FTIR) microspectroscopy. This method has been applied to analyze biological tissue with multivariate statistical analysis to classify the components revealed by the spectral data. This study aims to compare the efficiencies of SR-FTIR microspectroscopy and focal plane array (FPA)-FTIR microspectroscopy when classifying rice tissue components. Spectral data were acquired for mapping the same sample areas from both techniques. Principal component analysis and cluster imaging were used to investigate the biochemical variations of the tissue types. The classification was based on the functional groups of pectin, protein, and polysaccharide. Four layers from SR-FTIR microspectroscopy including pericarp, aleurone layer, sub-aleurone layer, and endosperm were classified using cluster imaging, while FPA-FTIR microspectroscopy could classify only three layers of pericarp, aleurone layer, and endosperm. Moreover, SR-FTIR microspectroscopy increased the image contrast of the biochemical distribution in rice tissue more efficiently than FPA-FTIR microspectroscopy. We have demonstrated the capability of the high-resolution synchrotron technique and its ability to clarify small structures in rice tissue. The use of this technique might increase in future studies of tissue characterization.

Novel infrared puffing: Effect on physicochemical attributes of puffed rice ( Oryza sativa L.)

The effect of novel infrared (IR) puffing and various IR powers (350, 450, and 550 Watts [W]) at various distances (10, 20, and 30 cm) on physicochemical characteristics of puffed rice (puffing properties, color, total phenolic content [TPC], antioxidant activity, peroxide value, and morphology) was investigated. By reducing the distance and increasing the IR power, the volume puffing was significantly increased (p < .05), and bulk density was significantly decreased (p < .05) but there was no significant difference in the length/breadth ratio. The IR puffing effect on color, the TPC, antioxidant activity, and food compounds' analysis through Fourier transform infrared (FTIR) spectra were significant (p < .05) during IR puffing. The scanning electron microscopy (SEM) images showed that by increasing the IR power and decreasing the sample distance from the IR source, the size of protrusions was increased (the volume of the protrusions). The maximum increase in the protrusions size was observed in 10 cm distance and 550 W power of IR. This is the first report on the IR puffing of rice and according to the results, the IR puffing technology has a high efficiency at the rice puffing.

Geographic Origin Discrimination of Millet Using Vis-NIR Spectroscopy Combined with Machine Learning Techniques

Millet is a primary food for people living in the dry and semi-dry regions and is dispersed within most parts of Europe, Africa, and Asian countries. As part of the European Union (EU) efforts to establish food originality, there is a global need to create Protected Geographical Indication (PGI) and Protected Designation of Origin (PDO) of crops and agricultural products to ensure the integrity of the food supply. In the present work, Visible and Near-Infrared Spectroscopy (Vis-NIR) combined with machine learning techniques was used to discriminate 16 millet varieties (n = 480) originating from various regions of China. Five different machine learning algorithms, namely, K-nearest neighbor (K-NN), Linear discriminant analysis (LDA), Logistic regression (LR), Random Forest (RF), and Support vector machine (SVM), were used to train the NIR spectra of these millet samples and to assess their discrimination performance. Visible cluster trends were obtained from the Principal Component Analysis (PCA) of the spectral data. Cross-validation was used to optimize the performance of the models. Overall, the F-Score values were as follows: SVM with 99.5%, accompanied by RF with 99.5%, LDA with 99.5%, K-NN with 99.1%, and LR with 98.8%. Both the linear and non-linear algorithms yielded positive results, but the non-linear models appear slightly better. The study revealed that applying Vis-NIR spectroscopy assisted by machine learning technique can be an essential tool for tracing the origins of millet, contributing to a safe authentication method in a quick, relatively cheap, and non-destructive way.

Non-Targeted LC-MS Metabolomics Approach towards an Authentication of the Geographical Origin of Grain Maize (Zea mays L.) Samples

Safety along the food and feed supply chain is an emerging topic and closely linked to the ability to analytical trace the geographical origin of food or feed. In this study, ultra-performance liquid chromatography coupled with electrospray ionization quadrupole-time-of-flight mass spectrometry was used to trace back the geographical origin of 151 grain maize (Zea mays L.) samples from seven countries using a high resolution non-targeted metabolomics approach. Multivariate data analysis and univariate statistics were used to identify promising marker features related to geographical origin. Classification using only 20 selected markers with the Random Forest algorithm led to 90.5% correctly classified samples with 100 times repeated 10-fold cross-validation. The selected markers were assigned to the class of triglycerides, diglycerides and phospholipids. The marker set was further evaluated for its ability to separate between one sample class and the rest of the dataset, yielding accuracies above 89%. This demonstrates the high potential of the non-polar metabolome to authenticate the geographic origin of grain maize samples. Furthermore, this suggests that focusing on only a few lipids with high potential for grain maize authentication could be a promising approach for later transfer of the method to routine analysis.

Food Phenotyping: Recording and Processing of Non-Targeted Liquid Chromatography Mass Spectrometry Data for Verifying Food Authenticity

Experiments based on metabolomics represent powerful approaches to the experimental verification of the integrity of food. In particular, high-resolution non-targeted analyses, which are carried out by means of liquid chromatography-mass spectrometry systems (LC-MS), offer a variety of options. However, an enormous amount of data is recorded, which must be processed in a correspondingly complex manner. The evaluation of LC-MS based non-targeted data is not entirely trivial and a wide variety of strategies have been developed that can be used in this regard. In this paper, an overview of the mandatory steps regarding data acquisition is given first, followed by a presentation of the required preprocessing steps for data evaluation. Then some multivariate analysis methods are discussed, which have proven to be particularly suitable in this context in recent years. The publication closes with information on the identification of marker compounds.

Food Authentication: Truffle (Tuber spp.) Species Differentiation by FT-NIR and Chemometrics

Truffles are certainly the most expensive mushrooms; the price depends primarily on the species and secondly on the origin. Because of the price differences for the truffle species, food fraud is likely to occur, and the visual differentiation is difficult within the group of white and within the group of black truffles. Thus, the aim of this study was to develop a reliable method for the authentication of five commercially relevant truffle species via Fourier transform near-infrared (FT-NIR) spectroscopy as an easy to handle approach combined with chemometrics. NIR-data from 75 freeze-dried fruiting bodies were recorded. Various spectra pre-processing techniques and classification methods were compared and validated using nested cross-validation. For the white truffle species, the most expensive Tuber magnatum could be differentiated with an accuracy of 100% from Tuber borchii. Regarding the black truffle species, the relatively expensive Tuber melanosporum could be distinguished from Tuber aestivum and the Chinese truffles with an accuracy of 99%. Since the most expensive Italian Tuber magnatum is highly prone to fraud, the origin was investigated and Italian T. magnatum truffles could be differentiated from non-Italian T. magnatum truffles by 83%. Our results demonstrate the potential of FT-NIR spectroscopy for the authentication of truffle species.

Food authentication in real life: How to link nontargeted approaches with routine analytics?

In times of increasing globalization and the resulting complexity of trade flows, securing food quality is an increasing challenge. The development of analytical methods for checking the integrity and, thus, the safety of food is one of the central questions for actors from science, politics, and industry. Targeted methods, for the detection of a few selected analytes, still play the most important role in routine analysis. In the past 5 years, nontargeted methods that do not aim at individual analytes but on analyte profiles that are as comprehensive as possible have increasingly come into focus. Instead of investigating individual chemical structures, data patterns are collected, evaluated and, depending on the problem, fed into databases that can be used for further nontargeted approaches. Alternatively, individual markers can be extracted and transferred to targeted methods. Such an approach requires (i) the availability of authentic reference material, (ii) the corresponding high-resolution laboratory infrastructure, and (iii) extensive expertise in processing and storing very large amounts of data. Probably due to the requirements mentioned above, only a few methods have really established themselves in routine analysis. This review article focuses on the establishment of nontargeted methods in routine laboratories. Challenges are summarized and possible solutions are presented.

Geographical Origin Authentication of Agri-Food Products: Α Review

This study is a systematic literature review of geographical origin authentication by elemental analytical techniques. Authentication and certification of geographic origin of agri-food products is a useful tool toward the protection of the quality for products. The aim of this work was to map the current state of research in the area of agricultural products and food, identifying emerging fields to the geographical origin of products. The article is divided in three parts. The first part of the article deals with the analytical techniques applied in the food authentication. Special mention is made to elemental analysis and multiple isotope ratio. The second section focuses on statistically published data concerning published research for geographical origin authentication for the period 2015-2019. Specific results are presented inter alia: number of articles according to the type of product, articles according to the type of the analytical techniques, and others. The third part contains characteristic results from articles that were published in the period 2015-2019, on certification of geographical origin on specific agricultural products.