RESUMO
A 5-scale label that relativizes the environmental impact of a given product referred to the impact of the European food basket is proposed. It was developed based on the Product Environmental Footprint methodology with the following stepwise approach. First, a set of normalization and weighting factors were defined to aggregate all the environmental impact categories into a single dimensionless index referred to as the European food basket, coined the European Food Environmental Footprint Single Index (EFSI). Next, the effectiveness of the EFSI index was evaluated by assessing the distribution of the EFSI results on 149 hypothetical food items and comparing it with the results obtained with EC Single Score. Finally, the thresholds to translate the EFSI index into the 5-scale Enviroscore (A, B, C, D, and E) were established and validated using the Delphi method. Results indicated that both, Enviroscore and EFSI, were able to account for impact variability between and within food products. Differences on the final score were observed due to the type of products (vegetables vs. animal products), the country of origin and the mean of transportation. Regarding country of origin, results indicated that differences in water stress impact category were better captured by the EFSI index (r = 0.624) than by the EC Single Score (r = 0.228). Finally, good agreement achieved with the Delphi method (weighted Kappa 0.642; p = 0.0025), ensures the acceptability of the Enviroscore. In conclusion, this study developed a method to communicate environmental impact assessment in a front-of-packaging label.
RESUMO
A handheld near infrared (NIR) spectrometer was used for on-site determination of the fatty acids (FAs) composition of industrial fish oils from fish by-products. Partial least square regression (PLSR) models were developed to correlate NIR spectra with the percentage of saturated fatty acids (SFAs), monounsaturated fatty acids (MUFAs), polyunsaturated fatty acids (PUFAs) and, among them, omega-3 (ω-3) and omega-6 (ω-6) FAs. In a first step, the data were divided into calibration validation datasets, obtaining good results regarding R2 values, root mean square error of prediction (RMSEP) and bias. In a second step, all these data were used to create a new calibration, which was uploaded to the handheld device and tested with an external validation set in real time. Evaluation of the external test set for SFAs, MUFAs, PUFAs and ω-3 models showed promising results, with R2 values of 0.98, 0.97, 0.97 and 0.99; RMSEP (%) of 0.94, 1.71, 1.11 and 0.98; and bias (%) values of -0.78, -0.12, -0.80 and -0.67, respectively. However, although ω-6 models achieved a good R2 value (0.95), the obtained RMSEP was considered high (2.08%), and the bias was not acceptable (-1.76%). This was corrected by applying bias and slope correction (BSC), obtaining acceptable values of R2 (0.95), RMSEP (1.09%) and bias (-0.05%). This work goes a step further in the technology readiness level (TRL) of handheld NIR sensor solutions for the fish by-product recovery industry.
RESUMO
The performances of three non-destructive sensors, based on different principles, bioelectrical impedance analysis (BIA), near-infrared spectroscopy (NIR) and time domain reflectometry (TDR), were studied to discriminate between unfrozen and frozen-thawed fish. Bigeye tuna (Thunnus obesus) was selected as a model to evaluate these technologies. The addition of water and additives is usual in the fish industry, thus, in order to have a wide range of possible commercial conditions, some samples were injected with different water solutions (based on different concentrations of salt, polyphosphates and a protein hydrolysate solution). Three different models, based on partial least squares discriminant analysis (PLS-DA), were developed for each technology. This is a linear classification method that combines the properties of partial least squares (PLS) regression with the classification power of a discriminant technique. The results obtained in the evaluation of the test set were satisfactory for all the sensors, giving NIR the best performance (accuracy = 0.91, error rate = 0.10). Nevertheless, the classification accomplished with BIA and TDR data resulted also satisfactory and almost equally as good, with accuracies of 0.88 and 0.86 and error rates of 0.14 and 0.15, respectively. This work opens new possibilities to discriminate between unfrozen and frozen-thawed fish samples with different non-destructive alternatives, regardless of whether or not they have added water.