The consumption of ready-to-eat (RTE) leafy vegetables has increased rapidly due to changes in consumer diet. RTE products are perceived as fresh, high-quality, and health-promoting. The monitoring of the RTE quality is crucial in relation to safety issues. This study aimed to evaluate the maintenance of RTE rocket salad freshness packed under modified atmospheres. A portable E-nose, the electrolyte leakage test (which measures the index of leaf damage—I_LD), and NIR spectroscopy and Aquaphotomics were employed. Two trials were carried out, using the following gas mixtures: (A) atmospheric air (21% O₂, 78% N₂); (B) 30% O₂, 70% N₂; (C) 10% CO₂, 5% O₂, 85% N₂. Samples were stored at 4 °C and analyzed at 0, 1, 4, 7, 11, and 13 days. ANOVA, PCA, PLS were applied for data processing. E-nose and I_LD results identified the B atmosphere as the best for maintaining product freshness. NIR spectroscopy was able to group the samples according to the storage time. Aquaphotomics proved to be able to detect changes in the water structure during storage. These preliminary data showed a good agreement NIR/I_LD suggesting the use of NIR for non-destructive monitoring of the damage to the plant membranes of RTE rocket salad.

Parallel transformation tests on pineapple slices using two micro drying plants (M1 and M2) operating with solar energy were carried out. Method M1 consisted of an active fan at the top, whose ventilation rate depended on the internal temperature. Method M2 had a continuously working fan at the bottom. The dehydration performance of these two micro-plants was compared by collecting spectra from pineapple slices in reflectance mode (900–1600 nm) at three different times: (0) process start, (1) during the process [48 h] and (2) process end [56 h]. Simultaneously, dry matter, titratable acidity (SH°), pH and a_w (water activity) were measured. For these parameters, significant differences ( p < 0.05) were detected between the fresh (t = 0) and the dried product (t = 56). Near infrared (NIR) spectroscopic analysis was carried out according to previously published methods. Spectral data in the wavelength region from 1300 to 1550 nm underwent statistical processing to perform aquaphotomics evaluation and chemometric analysis using PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). The aquagrams highlighted differences among fresh, half-dried and dried slices where water molecules were highly organized between the water matrix coordinates C1 to C3 at t = 0 and C2 to C6 for the other evaluated times. The PCA could explain about 98% of the total variance in the PC1–PC3 scores plot. And the additional LDA classified the NIR spectra with an accuracy of 100, 98 and 83% for t = 0, t = 56-M1 and t = 56-M2, respectively. Such preliminary results suggest the applicability of Aquaphotomics and chemometrics for the continuous monitoring of fruit drying processes using an adequate NIR probe. Further experiments are already in progress.

Author Summary: The GRANIR project founded by the Grana Padano Protection Consortium and developed by CREA-ZA research centre is devoted to the development of a rapid and economic method for the chemical characterisation of Grana Padano PDO cheese based on near infrared (NIR) spectroscopy technology. For this purpose, the Consortium purchased several portable spectrometers XNIRTM (dinamica generale®, Poggio Rusco, MN, Italy), to be assigned to the Consortium staff for screening operations of production batches in the fire-branding step, in warehouses and at the packaging step, on cheese paste. To develop predictive models and to evaluate the performance of the portable instruments, 195 samples of Grana Padano were scanned directly on the whole open wheel, scanning both rind and cheese paste. Robust models were built for the prediction of dry matter, fat, fat/dry matter, proteins and proteins/dry matter content using average spectra of rind and paste and chemical data of cheese paste. Additional spectra acquired with two other instruments were included in order to make the models less sensitive to different instruments. Spectra of the same samples acquired at different temperatures (10, 16 and 25 C) were also added to the dataset in order to reduce the influence of temperature on prediction results. The obtained results showed a satisfactory predictive ability of the models built with portable NIR spectrometers, with respect to the chemical composition of Grana Padano cheese, showing root mean square errors in prediction comparable to that obtained with a Fourier-Transform NIR benchtop instrument. This allows the estimation of average cheese composition, at batch level, using multiple scans taken on a high number of wheels.