A portable Raman sensor for the rapid discrimination of olives according to fruit quality

Handheld Devices for Food Authentication and Their Applications: A Review

This review summarises miniaturised technologies, commercially available devices, and device applications for food authentication or measurement of features that could potentially be used for authentication. We first focus on the handheld technologies and their generic characteristics: (1) technology types available, (2) their design and mode of operation, and (3) data handling and output systems. Subsequently, applications are reviewed according to commodity type for products of animal and plant origin. The 150 applications of commercial, handheld devices involve a large variety of technologies, such as various types of spectroscopy, imaging, and sensor arrays. The majority of applications, ~60%, aim at food products of plant origin. The technologies are not specifically aimed at certain commodities or product features, and no single technology can be applied for authentication of all commodities. Nevertheless, many useful applications have been developed for many food commodities. However, the use of these applications in practice is still in its infancy. This is largely because for each single application, new spectral databases need to be built and maintained. Therefore, apart from developing applications, a focus on sharing and re-use of data and calibration transfers is pivotal to remove this bottleneck and to increase the implementation of these technologies in practice.

Determination of the geographical origin of maize (Zea mays L.) using mineral element fingerprints

BACKGROUND

Maize (Zea mays L.) is a staple cereal crop and feed crop throughout the world. In this article, a mineral element fingerprinting technique was applied to single out suitable element indicators to determine the geographical origin of maize. A total of 90 fresh maize samples were collected in 2107 from Jilin, Gansu, and Shandong provinces in China. The contents of 25 mineral elements in all maize samples were measured by inductively coupled plasma mass spectrometry (ICP-MS). The composition of mineral elements was analyzed by multivariate statistical analysis, including one-way analysis of variance (one-way ANOVA), principal component analysis (PCA), k-nearest neighbor (KNN) analysis, and stepwise linear discriminant analysis (SLDA).

RESULTS

As compared by one-way ANOVA, the contents of 19 mineral elements in maize samples were significantly different among three provinces. Principal component analysis based on these 19 elements could obtain preliminary visual classification groups of maize samples. K-nearest neighbor analysis produced a total correct classification rate of 83.9% on the training set, and 82.2% on the prediction set. The SLDA model, based on eight indicative elements (Na, Cr, Rb, Sr, Mo, Cs, Ba, and Pb) obtained a total correct classification rate of 92.2% with cross-validation.

CONCLUSION

The mineral element fingerprinting technique combined with multivariate statistical analysis could be a helpful method to identify the geographical origin of maize. © 2019 Society of Chemical Industry.

Use of Raman microspectroscopy to predict malting barley husk adhesion quality

Good quality husk-caryopsis adhesion is essential for malting barley, but that quality is influenced by caryopsis surface lipid composition. Raman spectroscopy was applied to lipid extracts from barley caryopses of cultivars with differential adhesion qualities. Principal component regression indicated that Raman spectroscopy can distinguish among cultivars with good and poor quality adhesion due to differences in compounds associated with adhesion quality.

Spectroscopy applied to feed additives of the European Union Reference Laboratory: a valuable tool for traceability

Feed additives need to be authorised to be placed on the market according to Regulation (EU) No. 1831/2003. Next to laying down the procedural requirements, the regulation creates the European Union Reference Laboratory for Feed Additives (EURL-FA) and requires that applicants send samples to the EURL-FA. Once authorised, the characteristics of the marketed feed additives should correspond to those deposited in the sample bank of the EURL-FA. For this purpose, the submitted samples were subjected to near-infrared (NIR) and Raman spectroscopy for spectral characterisation. These techniques have the valuable potential of characterising the feed additives in a non-destructive manner without any complicated sample preparation. This paper describes the capability of spectroscopy for a rapid characterisation of products to establish whether specific authorisation criteria are met. This study is based on the analysis of feed additive samples from different categories and functional groups, namely products containing (1) selenium, (2) zinc and manganese, (3) vitamins and (4) essential oils such as oregano and thyme oil. The use of chemometrics turned out to be crucial, especially in cases where the differentiation of spectra by visual inspection was very difficult.

Novel technologies for monitoring the in-line quality of virgin olive oil during manufacturing and storage

The quality of virgin olive oil is related to the agronomic conditions of the olive fruits and the process variables of the production process. Nowadays, food markets demand better products in terms of safety, health and organoleptic properties with competitive prices. Innovative techniques for process control, inspection and classification have been developed in order to to achieve these requirements. This paper presents a review of the most significant sensing technologies which are increasingly used in the olive oil industry to supervise and control the virgin olive oil production process. Throughout the present work, the main research studies in the literature that employ non-invasive technologies such as infrared spectroscopy, computer vision, machine olfaction technology, electronic tongues and dielectric spectroscopy are analysed and their main results and conclusions are presented. These technologies are used on olive fruit, olive slurry and olive oil to determine parameters such as acidity, peroxide indexes, ripening indexes, organoleptic properties and minor components, among others. © 2016 Society of Chemical Industry.

Chemometric applications to assess quality and critical parameters of virgin and extra-virgin olive oil. A review

Today virgin and extra-virgin olive oil (VOO and EVOO) are food with a large number of analytical tests planned to ensure its quality and genuineness. Almost all official methods demand high use of reagents and manpower. Because of that, analytical development in this area is continuously evolving. Therefore, this review focuses on analytical methods for EVOO/VOO which use fast and smart approaches based on chemometric techniques in order to reduce time of analysis, reagent consumption, high cost equipment and manpower. Experimental approaches of chemometrics coupled with fast analytical techniques such as UV-Vis spectroscopy, fluorescence, vibrational spectroscopies (NIR, MIR and Raman fluorescence), NMR spectroscopy, and other more complex techniques like chromatography, calorimetry and electrochemical techniques applied to EVOO/VOO production and analysis have been discussed throughout this work. The advantages and drawbacks of this association have also been highlighted. Chemometrics has been evidenced as a powerful tool for the oil industry. In fact, it has been shown how chemometrics can be implemented all along the different steps of EVOO/VOO production: raw material input control, monitoring during process and quality control of final product.

Application of Infrared and Raman Spectroscopy for the Identification of Disease Resistant Trees

New approaches for identifying disease resistant trees are needed as the incidence of diseases caused by non-native and invasive pathogens increases. These approaches must be rapid, reliable, cost-effective, and should have the potential to be adapted for high-throughput screening or phenotyping. Within the context of trees and tree diseases, we summarize vibrational spectroscopic and chemometric methods that have been used to distinguish between groups of trees which vary in disease susceptibility or other important characteristics based on chemical fingerprint data. We also provide specific examples from the literature of where these approaches have been used successfully. Finally, we discuss future application of these approaches for wide-scale screening and phenotyping efforts aimed at identifying disease resistant trees and managing forest diseases.

Dispersive and FT-Raman spectroscopic methods in food analysis

Raman spectroscopy is a powerful technique for molecular analysis of food samples.

Unmanned platform for long-range remote analysis of volatile compounds in air samples

This paper describes a long-range remotely controlled CE system built on an all-terrain vehicle. A four-stroke engine and a set of 12-V batteries were used to provide power to a series of subsystems that include drivers, communication, computers, and a capillary electrophoresis module. This dedicated instrument allows air sampling using a polypropylene porous tube, coupled to a flow system that transports the sample to the inlet of a fused-silica capillary. A hybrid approach was used for the construction of the analytical subsystem combining a conventional fused-silica capillary (used for separation) and a laser machined microfluidic block, made of PMMA. A solid-state cooling approach was also integrated in the CE module to enable controlling the temperature and therefore increasing the useful range of the robot. Although ultimately intended for detection of chemical warfare agents, the proposed system was used to analyze a series of volatile organic acids. As such, the system allowed the separation and detection of formic, acetic, and propionic acids with signal-to-noise ratios of 414, 150, and 115, respectively, after sampling by only 30 s and performing an electrokinetic injection during 2.0 s at 1.0 kV.

A new avenue for classification and prediction of olive cultivars using supervised and unsupervised algorithms

Various methods have been used to identify cultivares of olive trees; herein we used different bioinformatics algorithms to propose new tools to classify 10 cultivares of olive based on RAPD and ISSR genetic markers datasets generated from PCR reactions. Five RAPD markers (OPA0a21, OPD16a, OP01a1, OPD16a1 and OPA0a8) and five ISSR markers (UBC841a4, UBC868a7, UBC841a14, U12BC807a and UBC810a13) selected as the most important markers by all attribute weighting models. K-Medoids unsupervised clustering run on SVM dataset was fully able to cluster each olive cultivar to the right classes. All trees (176) induced by decision tree models generated meaningful trees and UBC841a4 attribute clearly distinguished between foreign and domestic olive cultivars with 100% accuracy. Predictive machine learning algorithms (SVM and Naïve Bayes) were also able to predict the right class of olive cultivares with 100% accuracy. For the first time, our results showed data mining techniques can be effectively used to distinguish between plant cultivares and proposed machine learning based systems in this study can predict new olive cultivars with the best possible accuracy.