The feasibility of non-destructive detection and classification of in-shell insect-infested almonds was examined by using multispectral imaging (MSI) technology combined with chemometrics. Differentiation of reflectance spectral data between intact and insect-infested almonds was attempted by using analytical approaches based on principal component analysis and support vector machines, classification accuracy rates as high as 99.1% in the calibration set and 97.5% in the prediction set were achieved. Meanwhile, the in-shell almonds were categorized into three classes (intact, slightly infested and severely infested) based on the degree of damage caused by insect infestation and were characterized quantitatively by the analysis of shell/kernel weight ratio. A three-class model for the identification of intact, slightly infested and severely infested almonds yielded acceptable classification performance (95.6% accuracy in the calibration set and 93.3% in the prediction set). These results revealed that MSI technology combined with chemometrics may be a promising approach for the non-destructive detection of hidden insect damage in almonds and could be used for industrial applications.

The use of fast and non-destructive techniques for identifying sugarcane varieties enables the development of automatic sorting systems, contributing towards improving pre-processing steps in the alcohol and sugar industries. In this context, principal component analysis (PCA), factorial discriminant analysis (FDA), stepwise forward discriminant analysis (SFDA) and partial least-squares discriminant analysis (PLS-DA) were used to classify four Brazilian sugarcane varieties based on visible/near infrared (Vis/NIR) spectral reflectance measurements (450–1000 nm range) of stalks. All wavelengths contributed towards discriminating the sugarcane varieties, but the 600–750 nm range was most relevant. When evaluating PCA results considering the four sugarcane varieties, two of them overlapped and it was only possible to use classifiers of three varieties. Factorial discriminant analysis, PLS-DA and SFDA reached correct classifications of 0.81, 0.82 and 0.74, respectively, when considering the external validation data and the four sugarcane varieties evaluated. Results showed that Vis/NIR spectroscopy combined with discriminating methods is a promising tool for non-destructive and fast sugarcane variety classification, which can be used in the agro-food industry or directly in the field.

The aim of the studies reported in the Research Communication was to develop a rapid spectroscopic technique as an alternative method for the classification and discrimination of milk sources by Fourier transform infrared spectroscopy (FTIR). Cow, sheep and water buffalo milk samples were collected from various local milk producers in Istanbul, Turkey. In addition, various brands of packaged milk were purchased locally. Spectrums were obtained according to milk species origin and binary mixtures prepared in increments of 10% (10, 20, 30, 40, 50, 60, 70, 80 and 90%) for each sample analysed in FTIR spectroscopy. A successful milk species (cow, sheep, and water buffalo) discrimination and classification were achieved utilising Hierarchical cluster and principle component analyses (PCA) on the basis of Euclidean distance and Ward's algorithm. Amide-I (1700–1600/cm) and Amide-II (1565–1520/cm) spectral bands were used in the chemometric method. The results of the study indicated that adulteration of milk samples can be quantitatively detected by the FTIR technique in a short time with high accuracy. In conclusion, this method could be used as a new alternative technique for routine analysis in authenticity control of milk species origin.

A suite of 21 elements, namely Al, As, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, La, Mg, Mn, Na, Ni, P, Pb, Sr, Ti, V and Zn, was investigated in a sediment core from the Ross Sea, Antarctica. The experimental results were treated by chemometric techniques. The elemental composition of core H2 was found to be mainly dominated by terrigenous elements, but it is also influenced by biological factors, such as the presence of corals. No evidence of anthropogenic contamination was observed, even in the top layer of the sediment. Furthermore, the modified Community Bureau of Reference (BCR) three-step sequential extraction procedure was applied to evaluate metal mobility and availability. The results confirmed the separation between higher and lower sections of the core and showed the presence of Fe as amorphous and crystalline oxide and of Mn mostly as nodules. The high percentages of metals extracted into the fourth fraction indicate their strong binding with the sediment matrix. Finally, BCR procedure was compared to Tessier's protocol which made it possible to distinguish between mobile and mobilizable fraction. Therefore, in general partitioning procedure must be chosen taking into account the nature of the sample and the aim of the research.

Real-time information about milk composition would be very useful for managing the milking process. Mid-infrared spectroscopy, which relies on fundamental modes of molecular vibrations, is routinely used for off-line analysis of milk and the purpose of the present study was to investigate the potential of attenuated total reflectance mid-infrared spectroscopy for real-time analysis of milk in milking lines. The study was conducted with 189 samples from over 70 cows that were collected during an 18 months period. Principal component analysis, wavelets and neural networks were used to develop various models for predicting protein and fat concentration. Although reasonable protein models were obtained for some seasonal sub-datasets (determination errors <~0·15% protein), the models lacked robustness and it was not possible to develop a model suitable for all the data. Determination of fat concentration proved even more problematic and the determination errors remained unacceptably large regardless of the sub-dataset analyzed or of the spectral intervals used. These poor results can be explained by the limited penetration depth of the mid-infrared radiation that causes the spectra to be very sensitive to the presence of fat globules or fat biofilms in the boundary layer that forms at the interface between the milk and the crystal that serves both as radiation waveguide and sensing element. Since manipulations such as homogenisation are not permissible for in-line analysis, these results show that the potential of mid-infrared attenuated total reflectance spectroscopy for in-line milk analysis is indeed quite limited.

The post-genomic technologies are generating vast quantities of data but many nutritional scientists are not trained or equipped to analyse it. In high-resolution NMR spectra of urine, for example, the number and complexity of spectral features mean that computational techniques are required to interrogate and display the data in a manner intelligible to the researcher. In addition, there are often multiple underlying biological factors influencing the data and it is difficult to pinpoint which are having the most significant effect. This is especially true in nutritional studies, where small variations in diet can trigger multiple changes in gene expression and metabolite concentration. One class of computational tools that are useful for analysing this highly multivariate data include the well-known ‘whole spectrum’ methods of principal component analysis and partial least squares. In this work, we present a nutritional case study in which NMR data generated from a human dietary Cu intervention study is analysed using multivariate methods and the advantages and disadvantages of each technique are discussed. It is concluded that an alternative approach, called feature subset selection, will be important in this type of work; here we have used a genetic algorithm to identify the small peaks (arising from metabolites of low concentration) that have been altered significantly following a dietary intervention.