Smart drying: use of sensors and machine learning for the supervision and control of drying processes

Globalization of market entails the availability of produces regardless their production date, pursued through innovation in products and processes to obtain meat, fish and fruit vegetables with improved shelf-life, organoleptic quality, nutritional value, safety and healthiness during the whole agrofood chain. Consequently, market value of perishable commodity mainly depends on the preservation method used to guarantee food stability and thus to delay physicochemical, biochemical and microbiological spoilage. Among processing methods, drying is one of the oldest, typical, effective and viable preservation process throughout the world, which allow to prevent food spoilage and decay through moisture removal. It is a relatively complex, dynamic, unsteady and nonlinear process that, when not optimized, may be responsible for (1) quality degradation of food and (2) energy wastage. Consequently, new drying technologies must be designed to assure valuable products at the lowest carbon footprint. Among emerging drying technologies, smart drying is one of the newest and promising ones. It has potential to guarantee high-value end products, while enhancing drying efficiency, by implementing innovative and reliable sensors, resources, tools and practices. Moreover, smart drying can be cost-effective in both real-time monitoring of foodstuffs quality and dynamic controlling of operating conditions along the whole drying process. Smart drying is a multi- and inter-disciplinary sector and its recent developments embrace the following R&D areas: artificial intelligence, biomimetic, computer vision, microwave/dielectric spectroscopy, visible and near-infrared spectroscopy, hyper/multispectral imaging, magnetic resonance imaging, ultrasound imaging, electrostatic sensing and control system for the drying environment

STUDY OF SOME PRETREATMENTS FOR OPTIMIZING THE DEHYDRATION OF BIOLOGICAL CARROTS

The experimental work proposed has tested the use of some pre-treatments to optimize the dehydration of biological carrots (var. Romance) The pre-treatments used were: 1) blanching (95°C) for 1.5 min, 2) blanching (70°) with citric acid 2% (w/v) for 2 min, 3) dipping in pectin’s 1% (w/v) for 1 min, 4) dipping in pectin’s 1% (w/v) with vacuum impregnation technology for 10 sec at 734 mbar. The aim of this work was to evaluate and compare the effects on carrot quality attributes. For this goal, the trial involved the use of various analytical techniques such as the enzyme activity test for peroxidase (POD), colorimetric analysis, determination of the total phenol content, extraction and quantification of carotenoids, rehydration process analysis. In addition, four semi-theoretical mathematical models were used to describe the pattern of dehydration of the pre-treated carrot washers. In light of the results obtained, the blanching pre-treatment at 70°C with 2% citric acid (w/v) has preserved more than all the nutritional characteristics of the carrot during the dehydration process. This pre-treatment showed an almost complete inactivation of the enzyme peroxidase (POD), an average dehydration rate of 6 hours, a colour angle (h) with a tint tending to red tones (the visual appearance is appreciated by some consumers and therefore considered a positive attribute in the choice of the product). Analysis of the carotenoid and total phenol content showed that blanching at 70°C with 2% citric acid was, like control and immersion in 1% pectin’s, the best pre-treatment with less loss of quality components (carotenoids and phenols) during dehydration. The work has also shown that the Logarithmic model can be used as the only model to predict moisture loss for all the dehydration pre-treatments used in experimentation

NEW DIPPING TREATMENTS TO CONTROL ENZYMATIC BROWNING OF APPLES DURING DRYING

The present experimental activity aims to improve the quality of apple wedges (var. Golden delicious) during 8-h drying at 60°C, using dipping treatments in fruit juices (orange, pineapple, green kiwifruit, yellow kiwifruit) and/or herbal teas (green tea, dandelion, licorice, roselle). Preliminary tests allowed to select both pineapple and green kiwifruit juices as well as roselle (Hibiscus sabdariffa) dry extract as feasible pretreatments able to control enzymatic browning by reducing the polyphenol oxidase (PPO) activity. In fact, 70, 80 and 100% inhibition of PPO activity were achieved using green kiwifruit (5% v/v), pineapple (7.7% v/v) juices and roselle dry extract (1.5% w/v), respectively. The selected fruit juices, alone and in combination with roselle, were finally tested as pretreatment of the 8-h drying process. Product quality was evaluated at 0, 2, 4, 6 and 8 h of drying by monitoring changes in color, moisture content (g/gDW), soluble solid contents (SSC), water activity (aw) and total phenols content at (GAE/gDW). Dipping in both pineapple and kiwifruit juices avoids changes in color of apple wedges during the first 2 h of drying (i.e. product heating period) and allowed to obtain a final product with a 28% lower moisture content (0.16), as well as the highest SSC (7.41 °Brix) and total phenols content (354.70 GAE/gDW). Roselle dipping treatment substantially changed the hue angle of apple wedges to the crimson color, decreased the SSC (7.20 °Brix) and increased the total phenols content (> 415 GAE/gDW) of all samples. The best dipping treatment, in terms of final quality of the product, corresponded to the green kiwifruit juices, alone or in combination with roselle dry extract

EFFECT OF HOT-WATER BLANCHING IN TREHALOSE SOLUTIONS ON BOTH NUTRITIONAL AND TECHNOLOGICAL QUALITY OF SLICED ORGANIC CARROTS

The aim of the present work was to evaluate the effect of blanching treatments at 75 and 90°C for 3 min in trehalose solution (4% w/v) on nutritional and technological quality of organic carrot (cv. Romance) slices of 5-mm thickness. The following parameters were investigated: [1] residual enzymatic activities of peroxidase (POD) and pectin methylesterase (PME); [2] changes in color; [3] changes in firmness and elastic modulus; [4] total phenols content; [5] total carotenoids content; [6] soluble solids content (SSC); [7] electrolyte leakage and [8] radical scavenging activity (IC50). Statistical analysis was investigated through the principal component analysis (PCA), the analysis of variance (ANOVA) and the pairwise comparison (P<0.05). All the treatments retained better color than control sample, which seemed to blush after treatment and 30 min of exposure to air. Furthermore, thermal treatments showed lower values in the elastic modulus at higher temperature of treatment. However, blanching in trehalose solution seemed to be more effective in retaining the firmness of carrot slice than blanching in water. Both POD and PME residual enzyme activities decreased as the temperature of blanching increased and when the trehalose solution was used as dipping medium. Content of SSC, Ct and Ft showed minimal differences among treatments and control, while REL values were affected by thermal treatments. Finally, all samples showed very low radical scavenging activity. The first 3 principal components of PCA explained a total variance of 95.9% and allowed to distinguish 3 clusters: [1] control sample, [2] 75-°C blanching treatments and [3] 90-°C blanching treatments. In conclusion, the 4-% trehalose treatment was reliable in improving color and functional properties of blanched organic carrot slices

OPTIMIZATION OF THE ORGANIC FRUIT AND VEGETABLE DRYING PROCESS BY USING NON-DESTRUCTIVE TECHNIQUES

The aim of this project is the obtainment of a smart-prototype drier able to improve the hot-air drying process of organic fruits and vegetables using non-destructive technologies. Thus, a quality by design (QbD) approach has been followed in order to improve products quality and control strategy. For the intended purpose, both at-line near infrared spectroscopy (NIRS) and in-line computer vision (CV) techniques were tested. The data matrices were then subjected to chemometrics analysis in order to develop prediction and classification models able to follow up physico-chemical changes and recognise dehydration phases, respectively. Thermal (i.e. hot-water, microwave or steam blanching) and dipping (i.e. non-reducing sugar and/or ascorbic acid) pre-treatments were investigated as viable alternatives to reduce browning occurrence. Excellent performances (R2 = 0.91 - 0.98) were achieved in predicting physico-chemical changes (e.g. water activity, moisture content, soluble solid content, etc.) using NIRS coupled with partial least squares regression algorithm for both apple (var. Gala) and carrot (var. Romance). The prediction of colour changes using NIR wavelengths gave good results (R2 = 0.80 - 0.87), probably due to the fact that it is an indirect measurement. Features selection led to comparable prediction performances while reducing model complexity. Similarly, partial least squares discriminant analysis PLS-DA provided from good (> 0.85) to excellent (> 0.95) results in terms of sensitivity and specificity rates for the recognition of drying phases. Finally, computer-vision analysis showed potentiality in simultaneously monitoring morphological (e.g. area shrinkage and eccentricity), colour (CIELab) and physicochemical (moisture and drying rate) changes on apple cylinders during the process. Indeed, linear regression models gave excellent results (R2 = 0.993-0.999) in predicting changes in moisture content on the basis of the area shrinkage. The results confirm the feasibility of an accurate smart-control of the drying process based on non-destructive technology and set the basis for a scale up of the process

DEVELOPMENT OF PREDICTIVE MODELS FOR QUALITY CONTROL OF CARROTS DURING DRYING

This thesis research project is aimed at setting up prediction models based on NIR spectroscopy, for quality control of organic carrot discs (Daucus carota L., var. Romance) during hot-air drying process (horizontal flow) up to 8 h. Hot-water blanching was tested at 95°C for 1.5 min, as pre-treatment to control the occurrence of enzymatic browning during drying. Hot-water blanching had a positive impact on the appearance of the carrot discs. PLS regression showed good performances for the prediction of aw (RMSE = 0.04; R2 = 0.96), moisture (RMSE = 0.04; R2 = 0.98), SSC (RMSE = 4.32-4.40 °Brix; R2 =0.88), carotenoids (RMSE = 21.75-23.10; R2 = 0.96) and changes in color (RMSE = 1.40-1.46; R2 = 0.85-0.86) during drying. Also PLSDA classification showed very good metrics (total accuracy 92.38%) in recognising 3-drying steps, both for control and hot-water blanched samples. Features selection by iPLS and iPLSDA algorithms showed results better/equal than models based on full spectrum. For these results, the implementation of low-cost NIR sensors on drier device, seems feasible

Drying behavior of organic apples and carrots by using k-means unsupervised learning

Drying prevents food spoilage and decay through moisture removal due to simultaneous heat and mass transfer from food, which may be stored for long period with minimal deterioration occurring. However, drying technology is not always paired with good/excellent organoleptic, nutritional and/or functional properties of food. In fact, during drying the heat-sensitive substances are often destroyed and degradation processes may be exacerbated due to various and concurrent reaction mechanisms. Based on authors’ best knowledge, drying degradation kinetics of biological materials are usually pseudo first-order or first order reactions (i.e. carotenoids degradation in carrots) and may be affected by the initial quality of the product itself. Therefore, the main objective of the proposed study was to investigate the feasibility of k-means unsupervised learning to proactively monitor quality change in organic apples and carrots during hot-air drying. Based on authors’ best knowledge, fruit and vegetables drying has been widely addressed in literature; nevertheless, little insight is available on smart drying, while knowledge of its potential use in the organic sector is totally lacking

Real-time monitoring of apples (Malus domestica var. Gala) during hot-air drying using NIR spectroscopy

Among commercial fruits, apple shows a growing trend to its worldwide consumption, where dried apple plays a major part in food industry as raw material to produce snacks, integral breakfast foods, chips, etc., which have become popular in the diet of modern consumers in parallel with the human consumption of organic products. Despite apple tissue exhibits extensive and non-homogeneous discoloration during drying, it is nowadays often dried by conventional methods which, however, are usually uncontrolled and then prone to product quality deterioration. However, because no all conventional drying treatments are allowed by the European Organic Regulation (i.e. EC No. 834/2007 and EC No. 889/2008), drying of organic apples should be carefully optimized to obtain comparable results to conventional methods. Therefore, the main objective of the proposed study was to investigate the feasibility of near-infrared (NIR) spectroscopy as smart drying technology to proactively and non-destructively detect and monitor quality change in organic apple wedges during hot-air drying

Quality and drying behavior of organic fruit products

Drying prevents food spoilage and decay through moisture removal due to simultaneous heat and mass transfer from food, which may be stored for long period with minimal deterioration occurring. However, drying technology is not always paired with good/excellent organoleptic, nutritional and/or functional properties of food. In fact, during drying the heat-sensitive substances are often destroyed and degradation processes may be exacerbated due to various and concurrent reaction mechanisms. Based on authors’ best knowledge, drying degradation kinetics of biological materials are usually pseudo first-order or first order reactions (i.e. carotenoids degradation in carrots) and may be affected by the initial quality of the product itself. Authors refer to results from the impact of hot-water and microwave thermal pre-treatments on the drying behavior and the final quality of carrots (cv Romance) and apples (cv Gala), respectively. Pre-treatments significantly affect the hot-air drying periods, the final color, size, shape and texture. Results were useful to identify the drying phases as cluster by performing the unsupervised analysis of the state variables

DEVELOPMENT OF PREDICTIVE MODELS FOR QUALITY CONTROL OF GALA APPLES DURING DRYING

This thesis research project is aimed at setting up prediction models based on NIR spectroscopy, for quality control of organic apple wedges (Malus domestica B., var. Gala) during hot-air drying process (horizontal flow) up to 8 h. Hot-water and microwave blanching were both tested at 95°C for 5 min and 850 W for 45 sec, respectively, as pre-treatments to control the occurrence of enzymatic browning during drying. However, hot-water blanching had a negative impact on the appearance of the apple wedges, which were subjected to non-enzymatic discoloration (e.g. Maillard’s reaction). PLS regression showed good performances for the prediction of aw (RMSE = 0.03-0.04; R2 = 0.97-0.98), moisture (RMSE = 0.04-0.05; R2 = 0.97-0.98), SSC (RMSE = 4.54-4.99 °Brix; R2 = 0.96-0.97) and changes in chroma (RMSE = 2.31-2.75; R2 = 0.81-0.86) during drying. Also PLSDA classification showed very good metrics (total accuracy > 95%) in recognising 3-drying steps, both for control and microwave-treated samples. Features selection by iPLS and iPLSDA algorithms showed results better/equal than models based on full spectrum. For these results, the implementation of low-cost NIR sensors on drier device, seems feasible