Incorporation of Alpha‐Linolenic Acid and Enhancement of n‐3 Fatty Acids in Nile Tilapia: a Factorial Design

A 2² factorial design (two factors at two levels, in triplicate) was performed to investigate the influence of factors A (time of treatment, 15 and 30 days) and B (chia oil content in a supplemented diet, at 2.1 and 4.2 %) in three responses of interest referring to: (a) the incorporation of alpha‐linolenic acid (LNA) in lipids of Nile tilapia fillet; (b) the enhancement of n‐3 fatty acids; and (c) the decrease in the omega‐6/omega3 (n‐6/n‐3) ratio in fish. Factors A and B were significant in the three regression models obtained and the interaction AB was a significant contributor to the LNA and n‐6/n‐3 ratio. Analysis of variance suggested three significant and predictive mathematical models. Response surfaces analyses from designs indicated higher LNA and n‐3 contents and a lower n‐6/n‐3 ratio using both factors A and B in the higher levels (30 days of treatment and 4.2 % of chia oil in the diet for fish) chosen for this study.     

Environmentally Friendly Determination of Quality Parameters of Biodiesel/Diesel Blends Using Fourier Transform Infrared Spectra

Multivariate calibration models based on data from mid‐infrared spectroscopy of biodiesel/diesel blends were obtained. The blends were prepared from diesel oil and esters of soybean oil, waste cooking oil, and hydrogenated vegetable oil in proportions ranging from 0 to 100 % biodiesel. The results showed that the multivariate regression models with interval partial least squares (iPLS), backward interval partial least squares (biPLS), and synergy interval partial least squares (siPLS) were able to determine the fractions of the infrared spectrum that contain the relevant information for estimating the values of physicochemical properties, flash point, specific gravity, and cetane number, which are used in quality control of the blends. In the best models, the values of determination coefficients were greater than 0.9500, proving their efficiency as an alternative to traditional analytical methods.     

Chemometric Studies on zNose™ and Machine Vision Technologies for Discrimination of Commercial Extra Virgin Olive Oils

The aim of this study was to classify Turkish commercial extra virgin olive oil (EVOO) samples according to geographical origins by using surface acoustic wave sensing electronic nose (zNose?) and machine vision system (MVS) analyses in combination with chemometric approaches. EVOO samples obtained from north and south Aegean region were used in the study. The data analyses were performed with principal component analysis class models, partial least squares‐discriminant analysis (PLS‐DA) and hierarchical cluster analysis (HCA). Based on the zNose? analysis, it was found that EVOO aroma profiles could be discriminated successfully according to geographical origin of the samples with the aid of the PLS‐DA method. Color analysis was conducted as an additional sensory quality parameter that is preferred by the consumers. The results of HCA and PLS‐DA methods demonstrated that color measurement alone was not an effective discriminative factor for classification of EVOO. However, PLS‐DA and HCA methods provided clear differentiation among the EVOO samples in terms of electronic nose and color measurements. This study is significant from the point of evaluating the potential of zNose? in combination with MVS as a rapid method for the classification of geographically different EVOO produced in industry.     

Portable Infrared Spectrometer to Characterize and Differentiate Between Organic and Conventional Bovine Butter

The performance of a portable infrared system combined with pattern recognition to discriminate between organically and conventionally produced bovine butter samples as well as to predict the levels of conjugated linoleic acid (CLA) were evaluated. Sixty butter (27 organic and 33 conventional) samples were used in this study. Bovine butter–fat were applied onto an attenuated total reflectance infrared (ATR‐IR) accessory equipped with a five‐bounce ZnSe crystal set at 65 °C for spectral collection. In addition, ATR‐IR spectra of bovine butter were directly collected at room temperature to avoid phase separation. The fatty acid profile and the levels of CLA were determined using reference FAME‐GC‐FID analysis. SIMCA models showed well separated clusters that discriminated between organic and conventional bovine butters due to C=C trans bending out of the plane vibration modes band at 967 cm^?1. Additionally, strong PLSR models were developed to predict CLA levels using butter–fat and bovine butter spectra with SEP of 0.05 % and RPD of 4.7, indicating that the models are suitable for quality control applications. Portable IR technology offers the ability for “in situ” analysis of butters that is much less time consuming than current analytical practices for authentication and quality control efforts by the industry.     

Quantification of soluble solids in reconstituted açaí (Euterpe oleracea Mart.) pulp using near‐infrared spectroscopy

Açaí consumption is increasing worldwide because of the growing recognition of its nutritional and therapeutic properties. This product is classified based on its soluble solids content (SS), but the determination of SS in pulp is time consuming, tedious and not suitable for modern food processing plants. As near‐infrared (NIR) systems have been implemented to measure various quality attributes of food products, the objective of this study was to evaluate the feasibility of NIR diffuse reflectance spectroscopy to quantify the SS content of açaí pulp. Partial least squares (PLS) regression models were constructed to predict the SS. An optimum PLS model required one latent variable [principal component (PC)1 = 97%] with a root‐mean‐square error of calibration (RMSEC) of 1.06% for the calibration data set and the root‐mean‐square error of prediction (RMSEP) of 1.03% for internal cross‐validation. External validation using an independent data set showed good performance (RMSEP = 1.33% and Rp² = 0.82). NIR spectroscopy is a reliable method with which to determine SS in açaí pulp and thereby to classify açaí pulp according to established minimum quality standards.     

Use of multivariate analysis for the improvement in prediction accuracy of bacterial aerobic plate count by flow cytometry

Flow cytometry (FCM) and aerobic plate count (APC) by the culture method were performed on green tea samples spiked with Escherichia coli type strain NCTC9001 (ATCC11775) solutions of different concentrations. In FCM, fluorescence signals from multiple stained bacteria and other fluorophores are detected using detector channels, and recorded as events with a voltage at each channel. FCM data were analyzed in two ways: conventional and multivariate analysis. In the former, the number of events with voltages larger than the defined threshold values was regarded as the predicted APC. In the latter, voltage histograms of all channels were obtained and merged horizontally to serve as explanatory variables. Then a partial least squares regression (PLSR) model was built to predict APC from the histogram data. The coefficient of determination (R²) and the root mean square error (RMSE) between APC by the culture method and that predicted by conventional FCM were 0.916 and 1.08 cfu/ml². The APC values predicted by the PLSR model and those measured were in good agreement with R² of 0.982 and RMSE of 0.417 cfu/ml, which verified the potential of the proposed method for improving APC prediction accuracy by FCM.     

Developing a Vis/NIR spectroscopic system for fast and non-destructive pesticide residue monitoring in agricultural product

In this research, an optical system based on fibre optic Vis/NIR spectroscopy combined with chemometrics methods and software as a graphical user interface (GUI) was developed and presented for fast and non-destructive detection and determination of pesticide residues in agricultural products (a case study on diazinon in intact cucumbers). Vis/NIR spectra of cucumber samples without and with different concentrations of diazinon residue were analyzed at the range of 450–1000 nm. Partial least squares (PLS) regression models were developed based on chemical reference measurements and the spectral information of the samples after performing different pre-processing methods. Moreover, partial least squares-discriminant analysis (PLS-DA) models were developed based on different spectral pre-processing techniques to classify cucumbers with contents of diazinon below and above the maximum residue limits (MRL) as safe and unsafe samples, respectively. Finally, user-friendly software as a GUI was created based on the best PLS and PLS-DA models developed for prediction of diazinon contents in the samples and for classification of intact cucumbers by the absence/presence of diazinon residues, respectively. Evaluation of the system and software designed based on the best developed PLS and PLS-DA models indicated good performance for measuring and detection of diazinon residue in cucumbers. It was concluded that the designed system and software based on Vis/NIR spectroscopy combined with chemometrics methods can be utilized for fast and non-destructive safety control of intact cucumbers by the absence/presence of diazinon residues. It can also be generalized for detection of other pesticide residues in agricultural products if developing their appropriate models is feasible.     

Solid-phase microextraction gas chromatography–mass spectrometry (HS-SPME-GC–MS) determination of volatile compounds in orujo spirits: Multivariate chemometric characterisation

A headspace solid-phase microextraction gas chromatography mass spectrometric procedure (HS-SPME-GC–MS) was developed and applied in order to determine 22 volatile compounds (including alcohols, esters, aldehydes and terpenes) in different orujo spirit samples from the Geographic Denomination “Orujo de Galicia/Augardente de Galicia”. The orujo samples considered in this study were elaborated from Albariño variety grapes grown in the Rías Baixas restricted geographical area, and Albariño variety grapes grown in other geographical areas of Galicia (NW Spain) using two of the traditional distillation techniques: alembic and steam distillation. HS-SPME adequate results were obtained using a 65 μm carbowax-divinylbencene fibre during a headspace extraction at 40 °C with constant magnetic stirring for 15 min, and after a 5 min period of pre-equilibrium time. Desorption was performed directly in the gas chromatograph injector port for 5 min at 250 °C using the splitless mode. The applied method was demonstrated to be sensible, accurate, precise, and linear over more than one order of magnitude. Multivariate chemometric techniques (such as cluster analysis, principal component analysis and linear discriminant analysis) were used to characterise the orujo samples according to the geographical origin of the grapes and the distillation system employed in their elaboration on the basis of the chemical information provided for their volatile composition data.