Real-time monitoring of organic apple (var. Gala) during hot-air drying using near-infrared spectroscopy

Optimization of Processing Parameters for Continuous Microwave Drying of Crab Apple Slices via Response Surface Methodology

To improve product quality and obtain suitable processing parameters for crab apple slices (CASs) produced by continuous microwave drying (CMD), the effects of processing parameters, including slice thickness, microwave power, air velocity, and conveyor belt speed, on the evaluation indexes in terms of temperature, moisture content, color (L*, a*, b*), hardness, brittleness, and total phenolic content of CASs were investigated via the response surface method. The results indicated that microwave power has the greatest effect on the evaluation indexes applied to the CASs under CMD, followed by air velocity, slice thickness, and conveyor belt speed. To produce the desired product quality, the appropriate parameters for CMD of CASs were optimized as 1.25 mm slice thickness, 14,630 W microwave power, 0.50 m·s-1 air velocity, and 0.33 m·min-1 conveyor belt speed. Following that, the moisture content under CMD was found to be 13.53%, the desired color, hardness 0.79 g, brittleness 12.97 (number of peaks), and the total phenolic content 5.48 mg·g-1. This research provides a theoretical framework for optimizing the processing parameters of CASs using the response surface method.

Aquaphotomics Monitoring of Lettuce Freshness during Cold Storage

Fresh-cut leafy vegetables are one of the most perishable products because they readily deteriorate in quality even during cold storage and have a relatively short shelf life. Since these products are in high demand, methods for rigorous quality control and estimation of freshness that are rapid and non-destructive would be highly desirable. The objective of the present research was to develop a rapid, non-destructive near-infrared spectroscopy (NIRS)-based method for the evaluation of changes during cold storage of lettuce using an aquaphotomics approach to monitor the water molecular structure in lettuce leaves. The reference measurements showed that after 6 days of dark, cold storage, the weight and water activity of lettuce leaves decreased and β-carotene decreased, while chlorophylls slightly increased. Aquaphotomics characterization showed large differences in the lettuce leaves' spectra depending on their growth zone. Difference spectra, principal component analysis (PCA) and linear discriminant analysis (LDA) confirmed the differences in the inner and outer leaves and revealed that spectra change as a function of storage time. Partial least squares regression (PLSR) allowed the prediction of the time spent in storage with a coefficient of determination of R² = 0.80 and standard error of RMSE = 0.77 days for inner, and R² = 0.86 and RMSE = 0.66 days for outer leaves, respectively. The following water absorbance bands were found to provide the most information in the spectra: 1348, 1360, 1373, 1385, 1391, 1410, 1416, 1422, 1441, 1447, 1453, 1466, 1472, 1490, 1503, 1515, 1521, 1534 and 1571 nm. They were further used as water matrix coordinates (WAMACs) to define the water spectral patterns (WASPs) of lettuce leaves. The WASPs of leaves served to succinctly describe the state of lettuces during storage. The changes in WASPs during storage reveled moisture loss, damage to cell walls and expulsion of intracellular water, as well as loss of free and weakly hydrogen-bonded water, all leading to a loss of juiciness. The WASPs also showed that damage stimulated the defense mechanisms and production of vitamin C. The leaves at the end of the storage period were characterized by water strongly bound to collapsed structural elements of leaf tissues, mainly cellulose, leading to a loss of firmness that was more pronounced in the outer leaves. All of this information was reflected in the changes of absorbance in the identified WAMACs, showing that the water molecular structure of lettuce leaves accurately reflects the state of the lettuce during storage and that WASPs can be used as a multidimensional biomarker to monitor changes during storage.

Research and Application Validation of a Feature Wavelength Selection Method Based on Acousto-Optic Tunable Filter (AOTF) and Automatic Machine Learning (AutoML)

Near-infrared spectroscopy has been widely applied in various fields such as food analysis and agricultural testing. However, the conventional method of scanning the full spectrum of the sample and then invoking the model to analyze and predict results has a large amount of collected data, redundant information, slow acquisition speed, and high model complexity. This paper proposes a feature wavelength selection approach based on acousto-optical tunable filter (AOTF) spectroscopy and automatic machine learning (AutoML). Based on the programmable selection of sub nm center wavelengths achieved by the AOTF, it is capable of rapid acquisition of combinations of feature wavelengths of samples selected using AutoML algorithms, enabling the rapid output of target substance detection results in the field. The experimental setup was designed and application validation experiments were carried out to verify that the method could significantly reduce the number of NIR sampling points, increase the sampling speed, and improve the accuracy and predictability of NIR data models while simplifying the modelling process and broadening the application scenarios.

Antioxidant Activity and Discrimination of Organic Apples (Malus domestica Borkh.) Cultivated in the Western Region of Romania: A DPPH· Kinetics-PCA Approach

Apple (Malus domestica Borkh.) is one of the most used fruit for beverages in Romania. The goal of the study was to evaluate the antioxidant activity and discrimination of various parts of organic and non-organic apple varieties cultivated in the western region of Romania using the DPPH· kinetics–PCA (principal component analysis) approach. Organic and non-organic apples were subjected to solid–liquid ethanol extraction. Core and shell extracts were mixed with DPPH· and spectrophotometrically monitored at 517 nm. Antioxidant activity and mean DPPH· reaction rate at various time ranges reveal significant differences between organic and non-organic samples, as well as apple parts. Organic core and shell extracts had higher antioxidant activities than the corresponding non-organic samples (74.5–96.9% and 61.9–97.2%, respectively, 23.5–94.3% and 59.5–95.5%). Significant differences were observed for the DPPH· reaction rate for the first ½ min, especially in the presence of organic core extracts (3.7–4.8 μM/s). The organic samples were well discriminated by DPPH· kinetics–PCA, the most important variables being the DPPH· reaction rate for the first time range. This is the first DPPH· kinetics–PCA approach applied for discriminating between organic and non-organic fruits and can be useful for evaluating the quality of such type of fruits.

Recent developments in vibrational spectral analyses for dynamically assessing and monitoring food dehydration processes

Dehydration is one of the most widely used food processing techniques, which is sophisticated in nature. Rapid and accurate prediction of dehydration performance and its effects on product quality is still a difficult task. Traditional analytical methods for evaluating food dehydration processes are laborious, time-consuming and destructive, and they are not suitable for online applications. On the other hand, vibrational spectral techniques coupled with chemometrics have emerged as a rapid and noninvasive tool with excellent potential for online evaluation and control of the dehydration process to improve final dried food quality. In the current review, the fundamental of food dehydration and five types of vibrational spectral techniques, and spectral data processing methods are introduced. Critical overtones bands related to dehydration attributes in the near-infrared (NIR) region and the state-of-the-art applications of vibrational spectral analyses in evaluating food quality attributes as affected by dehydration processes are summarized. Research investigations since 2010 on using vibrational spectral technologies combined with chemometrics to continuously monitor food quality attributes during dehydration processes are also covered in this review.

Multi-Actor Governance for a Circular Economy in the Agri-Food Sector: Bio-Districts

The transition of the European agri-food sector towards a sustainable production and consumption model is a key element of the Green Deal. The new European “Farm to Fork” strategy aims to make the food system fair, healthy, and environmentally friendly. The consolidation and development of the organic model are two of the main objectives. In Italy, this development can be achieved through the Bio-District model. This model, which was born in the last ten years in Italy, is characterized by innovative multi-actor governance. From an explorative perspective, this study analyses the background literature on Bio-Districts and the context of the development of the Etruscan Roman Bio-District to understand the potential and the factors that allow the application of the principles of the Circular Economy. It focuses on multiple comparative analyses by using a qualitative–quantitative approach. The analysis of the context highlights the potential for expansion linked to an integrated short supply chain through three scenarios.

Physicochemical Fingerprint of "Pera Rocha do Oeste". A PDO Pear Native from Portugal

"Pera Rocha do Oeste" is a pear (Pyrus communis L.) variety native from Portugal with a Protected Designation of Origin (PDO). To supply the world market for almost all the year, the fruits are kept under controlled storage. This study aims to identify which classical physicochemical parameters (colour, total soluble solids (TSS), pH, acidity, ripening index, firmness, vitamin C, total phenols, protein, lipids, fibre, ash, other compounds including carbohydrates, and energy) could be fingerprint markers of PDO "Pera Rocha do Oeste". For this purpose, a data set constituting fruits from the same size, harvested from three orchards of the most representative PDO locations and stored in refrigerated conditions for 2 or 5 months at atmospheric conditions or for 5 months under a modified atmosphere, were selected. To validate the fingerprint parameters selected with the first set, an external data set was used with pears from five PDO orchards stored under different refrigerated conditions. Near infrared (NIR) spectroscopy was used as a complementary tool to assess the global variability of the samples. The lightness of the pulp; the b* CIELab coordinate of the pulp and peel; and the pulp TSS, pH, firmness, and total phenols, due to their lower variability, are proposed as fingerprint markers of this pear.

Optimisation of Physical and Chemical Treatments to Control Browning Development and Enzymatic Activity on Fresh-cut Apple Slices

Optimisation of processing time and pre-treatments are crucial factors prior to apple drying to produce a high-quality product. The purpose of the present study was to test the utility of physical (hot-water, HWB and steam blanching, SB) and chemical (1% ascorbic acid, AA; and 1% citric acid, CA) treatments, alone or in combination in reducing surface discolouration as well as oxidative enzyme activity in apple slices (cv. Golden Delicious and Elstar) exposed to air at room temperature for 0, 30 and 60 min. The total colour change (ΔE) for Golden Delicious was equal to 2.38, 2.68, and 4.05 after 0, 30 and 60 min of air exposure, respectively. Dipping in AA solution (1% w/v) was found to be the best treatment to limit surface discolouration of both apple cultivars. The best heat treatments to inhibit polyphenol oxidase/peroxidase enzymes activity were 70 °C HWB for Golden Delicious and 60 °C HWB for Elstar slices, both in combination with a solution of 1% AA and 1% CA. The tested apple cultivars were found to require different treatments at minimum ambient air exposure to obtain the best surface colour condition.

Indirect Convective Solar Drying Process of Pineapples as Part of Circular Economy Strategy

This study investigates the industrial-scale application of a simple convective solar drying process of pineapples as part of a circular economy strategy for developing countries. A renewable energy concept is presented, which follows the circular economy aims by effectively employing a simple system for biogas production and a two-stage drying system. Both these systems meet the requirements for implementation in the specific conditions of developing countries, of which Togo, where pineapple is a major crop, is taken as an example. With respect to earlier findings available in the literature, the paper focuses on the solar drying process, which is critical to the proposed strategy. A portable solar dryer working in indirect heating mode was built and later also modified to enhance its performance. Three main factors influencing the convective drying process, namely, drying time (270 min, 480 min), solar radiation intensity (650 W/m2, 1100 W/m2), and slice thickness (6–8 mm, 12–14 mm), were considered. The statistical Design of Experiments (DOE) method was applied to reduce the number and scope of experiments. In the best case, the moisture content was reduced from 87.3 wt % in fresh samples to 29.4 wt % in dried samples, which did not meet the quality requirements for dried fruit. An additional conventional post-solar drying procedure would, therefore, still be necessary. Nonetheless, the results show that in the case of pineapple drying the consumption of fossil fuels can be decreased significantly if convective solar pre-drying is employed.

Process Pattern-Based Near-Infrared Spectroscopy (NIRS) Fault Detection Using a Potential Function

This paper proposes a near-infrared (NIR) fault detection technology based on a process pattern via a potential function. Near-infrared spectroscopy is used to acquire process information at the molecular level. In this study, the process pattern concept is first introduced in the field of process control and a process pattern construction method based on elastic net-PCA is put forth. Next, the potential function discriminant method is applied to distinguish and classify the constructed process pattern and identify the running state of the industrial system. Finally, the proposed method is verified and analyzed using spectra data of the crude oil desalination and dehydration process. Compared with existing fault detection methods, the proposed approach offers the following advantages: (1) potential function discrimination achieves nonlinear process classification with better fault detection accuracy and good visualization performance; (2) fault detection based on NIR spectra is faster with and possesses greater accuracy because it acquires process information from a microscopic molecular perspective; and (3) the process pattern contains more effective process information and can more comprehensively characterize the essential features of processes.