Effect of air-dehydration pretreatment before freezing on the electrical impedance characteristics and texture of carrots

A comparative study regarding the adulteration detection of honey: Physicochemical parameters vs. impedimetric data

Honey adulteration is a major issue for European Union and its members because of an unfair practice of different producers and beekeepers, many adulterations involve the addition of sweet, concentrated syrups which may appear like honey. In our study we analysed the influence of adulteration of tilia honey with different syrups (such as corn, rice, inverted sugar, agave, maple syrups) in different percentages (5%, 10%, and 20% respectively) on physicochemical parameters (moisture content, L*, hab,cab, pH, free acidity, electrical conductivity (EC), 5-hydroxymetilfurfural (HMF), fructose, glucose, sucrose, turanose, trehalose, melesitose and raffinose) and impedimetric properties using electrochemical impedance spectroscopy. The impedimetric sensing was made using an electrochemical cell composed of two gold electrodes, and the frequency ranged between 0.1 kHz and 100 kHz. The impedimetric parameters (Z', Z″ and phase) and Randal circuit parameters can distinguish the authentic honeys from the adulterated ones (based on the adulteration agent and adulteration percentage, respectively). The partial least squares - discriminant analysis (PLS-DA) and support vector machines (SVM) were used in a binary mode to separate the authentic honeys from the adulterated ones, and the SVM proved to separate much better than PLS-DA.

Plant impedance spectroscopy: a review of modeling approaches and applications

Electrochemical impedance spectroscopy has emerged over the past decade as an efficient, non-destructive method to investigate various (eco-)physiological and morphological properties of plants. This work reviews the state-of-the-art of impedance spectra modeling for plant applications. In addition to covering the traditional, widely-used representations of electrochemical impedance spectra, we also consider the more recent machine-learning-based approaches.

Individual and synergistic effect of multi-frequency ultrasound and electro-infrared pretreatments on polyphenol accumulation and drying characteristics of edible roses

The freeze-dried (FD) edible roses with high content of bioactive substances and superior flavor have been favored by consumers. Nevertheless, the development of freeze-dried rose industry has been plagued by a long drying time and low efficiency. This study investigated the effects of ultrasonic pretreatment (UP) in multi-frequency modes and electro-infrared pretreatment (EIP) prior to FD on polyphenol accumulation and drying characteristics of roses. The mechanism was explored by the changes in microstructure, equivalent circuit parameters, and phenol identifications of rose. The results showed that the FD time of roses decreased by 26 % after ultrasonic-infrared sequential synergistic pretreatment (UP + EIP) due to the damage of cell membrane permeability from UP. The quality attributes of UP + EIP products including color, phenols, and antioxidant activity (DPPH and ABTS radical scavenging rates) remarkably improved. UP + EIP significantly (p < 0.05) increased the content of polyphenols, namely quercetin-3β-d-glucoside, phlorizin, procyanidin B₂, gallicacid, and rutin in the FD roses quantified by ultra-high performance liquid chromatography-quadrupole time of flight mass spectrometry (UPLC-qTOF-MS/MS). Therefore, UP + EIP is an effective pretreatment method for shortening FD time and producing high-quality FD rose products with enhanced polyphenol content.

Electric Impedance Spectroscopy in Trees Condition Analysis: Theory and Experiment

Electric impedance spectroscopy is an alternative technology to existing methods that shows promising results in the agro-food industry and plant physiology research. For example, this technology makes it possible to monitor the condition of plants, even in the early stages of development, and to control the quality of finished products. However, the use of electric impedance spectroscopy is often associated with the need to organize special laboratory conditions for measurements. Our aim is to extract information about the state of health of the internal tissues of a plant's branches from impedance measurements. Therefore, we propose a new technique using the device and model developed by us that makes it possible to monitor the condition of tree branch tissues in situ. An apple tree was chosen as the object under study, and the dependence of the impedance of the apple tree branch on the signal frequency and branch length was analyzed. The change in the impedance of an apple tree branch during drying was also analyzed. It was shown that, when a branch dries out, the conductivity of the xylem mainly decreases. The developed technique was also applied to determine the development of the vascular system of an apple tree after grafting. It was shown that the processing of the scion and rootstock sections with the help of cold atmospheric plasma and a plasma-treated solution contributes to a better formation of graft unions.

Effects of combined drying methods on physicochemical and rheological properties of instant Tremella fuciformis soup

The textural, rheological and flavor reconstitution behaviors of rehydrated instant Tremella fuciformis soup, treated by combined freeze drying + vacuum drying (FD-VD), freeze drying + far-infrared drying (FD-FIRD) and freeze drying + hot air drying (FD-HAD), were evaluated and compared with fresh soup. Moisture content transfer point (MTP) of 15 ± 2% was selected according to products quality and drying time. Instant soup treated by FD-VD showed lower shrinkage and looser structure compared with that of FD-FIRD and FD-HAD. After rehydration, FD-VD soup showed the best reconstitution behaviors with recovery rate of TSS, soluble protein and polysaccharide content up to 87.61%, 83.52%, 79.08% respectively. Besides, FD-VD rehydrated soup, with the highest polysaccharide content, showed similar rheological properties to the fresh soup. FD-VD soup contained more similar aroma compounds to FD sample measured by GC-MS, while FD-FIRD and FD-HAD generated new aldehydes, ketones and hydrocarbons, leading to unpleasant flavors.

Prediction of Date Fruit Quality Attributes during Cold Storage Based on Their Electrical Properties Using Artificial Neural Networks Models

Evaluating and predicting date fruit quality during cold storage is critical for ensuring a steady supply of high-quality fruits to meet market demands. The traditional destructive methods take time in the laboratory, and the results are based on one specific parameter being tested. Modern modeling techniques, such as Machine Learning (ML) algorithms, offer unique benefits in nondestructive methods for food safety detection and predicting quality attributes. In addition, the electrical properties of agricultural products provide crucial information about the interior structures of biological tissues and their physicochemical status. Therefore, this study aimed to use an alternative approach to predict physicochemical properties, i.e., the pH, total soluble solids (TSS), water activity (a_w), and moisture content (MC) of date fruits (Tamar), during cold storage based on their electrical properties using Artificial Neural Networks (ANNs), which is the most popular ML technique. Ten date fruit cultivars were studied to collect data for the targeted parameters at different cold storage times (0, 2, 4, and 6 months) to train and test the ANNs models. The electrical properties of the date fruits were measured using a high-precision LCR (inductance, capacitance, and resistance) meter from 10 Hz to 100 kHz. The ANNs models were compared with a Multiple Linear Regression (MLR) at all testing frequencies of the electrical properties. The MLR models were less accurate than ANNs models in predicting fruit pH and had low performance and weak predictive ability for the TSS, a_w, and MC at all testing frequencies. The optimal ANNs prediction model consisted of the input layer with 14 neurons, one hidden layer with 15 neurons, and the output layer with 4 neurons, which was determined depending on the measurements of the electrical properties at a 10 kHz testing frequency. This optimal ANNs model was able to predict the pH with R² = 0.938 and RMSE = 0.121, TSS with R² = 0.954 and RMSE = 2.946, a_w with R² = 0.876 and RMSE = 0.020, and MC with R² = 0.855 and RMSE = 0.803 b by using the measured electrical properties. The developed ANNs model is a powerful tool for predicting fruit quality attributes after learning from the experimental measurement parameters. It can be suggested to efficiently predict the pH, total soluble solids, water activity, and moisture content of date fruits based on their electrical properties at 10 kHz.

Monitoring Freeze-Damage in Grapefruit by Electric Bioimpedance Spectroscopy and Electric Equivalent Models

Grapefruit is a cold-sensitive citrus fruit, and freezing can spoil the harvest when the fruit is still on the tree and even later during manufacturing and transport due to inappropriate postharvest management. This study performed a specific Electric Impedance Spectroscopy (EIS) analysis and statistical data treatment to obtain an EIS and Artificial Neural Networks (ANN)-based model for early freeze-damage detection in grapefruit showing a Correct Correlation Rate of 100%. Additionally, Cryo-Field Emission Scanning Electron Microscopy observations were conducted on both fresh and frozen/thawed samples, analyzing the different impedance responses in order to understand the biological changes in the tissue. Finally, a modified Hayden electric equivalent model was parameterized to simulate the impedance response electrically and link the electric behavior of biological tissue to the change in its properties due to freezing. The developed technique is introduced as an alternative to the traditional ones, as it is fast, economic, and easy to carry out.

Impact of Processing Factors on Quality of Frozen Vegetables and Fruits

In this paper I review the production of frozen vegetables and fruits from a chain perspective. I argue that the final quality of the frozen product still can be improved via (a) optimization of the complete existing production chain towards quality, and/or (b) introduction of some promising novel processing technology. For this optimization, knowledge is required how all processing steps impact the final quality. Hence, first I review physicochemical and biochemical processes underlying the final quality, such as water holding capacity, ice crystal growth and mechanical damage. Subsequently, I review how each individual processing step impacts the final quality via these fundamental physicochemical and biochemical processes. In this review of processing steps, I also review the potential of novel processing technologies. The results of our literature review are summarized via a causal network, linking processing steps, fundamental physicochemical and biochemical processes, and their correlation with final product quality. I conclude that there is room for optimization of the current production chains via matching processing times with time scales of the fundamental physicochemical and biochemical processes. Regarding novel processing technology, it is concluded in general that they are difficult to implement in the context of existing production chains. I do see the potential for novel processing technology combined with process intensification, incorporating the blanching pretreatment—but which involves quite a change of the production chain.

Rapid detection of Atlantic salmon multi-quality based on impedance properties

To establish a rapid, convenient, and low-cost method to assess the quality of Atlantic salmon, we analyzed the impedance between 10-1 and 105 Hz for Atlantic salmon/rainbow trout, chilled/frozen-thawed salmon, and fresh/stale salmon. We combined chemometrics with impedance properties to create a multi-quality index for Atlantic salmon. The accuracy of all three models established can reach 100% in distinguishing Atlantic salmon from rainbow trout and distinguishing chilled salmon from frozen-thawed salmon. We applied a partial least squares method to create a quantitative prediction model of bioimpedance spectroscopy and the value of total volatile basic nitrogen. The correlation coefficients of the training and test sets were 0.9447 and 0.9387. Our results showed that the combination of impedance properties and chemometrics was a simple and effective application to evaluate Atlantic salmon quality.

Electrochemical impedance spectroscopy characterization of beverages

This paper compares the results of standard chemical analytical processes and electrochemical impedance spectroscopy (EIS) in the characterization of different beverages, namely ground coffee, soluble coffee, coffee substitutes, barley, cow milk, vegetable drinks, tea, plant infusions and plant mixtures. For the two approaches, the similarities between the experimental data are assessed by means of the Euclidean and Canberra distances. The resulting information is processed by means of the multidimensional scaling (MDS) clustering and visualization algorithm. The results of the chemical analytical processes and EIS reveal identical clusters for the two adopted distances. Furthermore, the robustness of the experimental and computational scheme are assessed by means of the Procrustes technique. The results confirm the effectiveness of combining the EIS and MDS.

Freeze-Damage Detection in Lemons Using Electrochemical Impedance Spectroscopy

Lemon is the most sensitive citrus fruit to cold. Therefore, it is of capital importance to detect and avoid temperatures that could damage the fruit both when it is still in the tree and in its subsequent commercialization. In order to rapidly identify frost damage in this fruit, a system based on the electrochemical impedance spectroscopy technique (EIS) was used. This system consists of a signal generator device associated with a personal computer (PC) to control the system and a double-needle stainless steel electrode. Tests with a set of fruits both natural and subsequently frozen-thawed allowed us to differentiate the behavior of the impedance value depending on whether the sample had been previously frozen or not by means of a single principal components analysis (PCA) and a partial least squares discriminant analysis (PLS-DA). Artificial neural networks (ANNs) were used to generate a prediction model able to identify the damaged fruits just 24 hours after the cold phenomenon occurred, with sufficient robustness and reliability (CCR = 100%).

Dehydrofreezing of peach: Blanching, D-sodium erythorbate vacuum infiltration, vacuum dehydration, and nitrogen packaging affect the thawed quality of peach

Peach slices were blanched (BL), vacuum infiltrated with D-sodium erythorbate (SE), predehydrated, and then nitrogen packaged (NP) before freezing to improve their quality. Our results showed that the BL, SE, and NP pretreatments remarkably improved the quality of frozen peaches. Frozen peaches pretreated by SE+NP+BL showed the highest total phenolic content (TPC), total antioxidant capacity (TAC), and 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging capacity after thawing at 20°C for 24 hr. The soluble solids content and firmness of low-maturity peaches dehydrated to 25% dehydration of their weight were 11.1% and 211.2% higher than those of the control samples, respectively, while their drip loss was 71.9% lower than that of the controls. In conclusion, pretreatment by BL, predehydration, SE, and NP before freezing can significantly improve the quality of frozen peaches after thawing. PRACTICAL APPLICATIONS: We believe that our study results have practical applications because the method of vacuum dehydration combined with blanching, nitrogen packaging, and D-sodium erythorbate treatment of peaches maintains their original taste, inhibits color change, and decreases drip loss. This method is suitable for fruit frozen and stored at a commercial freezing temperature of -20°C and does not need advanced equipment or technology. It can be easily carried out during the fruit freezing process and can be applied to other frozen stored fruits besides peaches.

Early Detection of Freeze Damage in Navelate Oranges with Electrochemical Impedance Spectroscopy

The early detection of freeze damage in Navelate oranges (Citrus sinensis L. Osbeck) was studied using electrochemical impedance spectroscopy (EIS), which is associated with a specific double-needle sensor. The objective was to identify this problem early in order to help to determine when a freeze phenomenon occurs. Thus, we selected a set of Navelate oranges without external defects, belonging to the same batch. Next, an intense cold process was simulated to analyze the oranges before and after freezing. The results of the spectroscopy analysis revealed different signals for oranges depending on whether they had experienced freezing or not. Principal Component Analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) of the obtained data demonstrated that it is possible to discriminate the samples, explaining 88.5% of the total variability (PCA) and being able to design a mathematical model with a prediction sensitivity of 80% (PLS-DA). Additionally, a designed artificial neural network (ANN) prediction model managed to correctly classify 100% of the studied samples. Therefore, EIS together with ANN-based data treatment is proposed as a viable alternative to the traditional techniques for the early detection of freeze damage in oranges.