Search

The feasibility of using a computer-vision (CV) system embedded in a hot-air dryer for non-destructive and real-time monitoring of the drying behaviour of organic apples was investigated in the present study. Apple cylinders were subjected to anti-browning treatments with different dipping solutions (water as control, trehalose (4% w/v) or trehalose + ascorbic acid (4% w/v and 1% w/v)) and dipping pressures (atmospheric, 101.3 kPa and sub-atmospheric pressure, 50 kPa) followed by drying at 60 °C to a final dry basis moisture content of 0.18 g g−1. The CV system was used as an in-line process analytical technology (PAT) tool to capture images reflecting the physico-chemical changes during product drying coupled with in-line mass changes and off-line reference analyses. The spatial and colour changes from the image analysis described well the complex and non-homogenous nature of apple drying. The results of spatial changes allowed successful development of accurate linear prediction models for moisture content as a function of area shrinkage (on scaled variables) with excellent prediction capability (|BIAS

The worldwide potato production is considered the fourth-most important food sector due to the increasing use of potatoes as raw materials for high-convenience food. Colour is a crucial quality attribute in fresh-cut potatoes, and enzymatic browning, due to polyphenols oxidase (PPO), is related to unacceptability by consumer. Among additives used to minimize discoloration, sulphites are very common, but affected by health-related hazards and marketing issues. Viable alternatives are thermal treatments, used to inactivate enzymes and improve product shelf-life. In this study, the efficacy of hot-water and steam blanching from 80 to 90°C, used to inactivate PPO in 1-cm potato slices, was evaluated in terms of inactivation kinetic, substrate specificity and transition state parameters. In general, all treatments inactivated PPO and reduced its kinetic efficiency. In detail, results from thermal inactivation kinetic promoted hot-water blanching at 90°C for approx. 2 min as the fastest treatment to obtain colour-stable potato slices.

Dried apple (Malus domestica B.) shows a growing trend to its worldwide consumption as raw material used to produce organic snacks, integral breakfast foods, chips, etc. Apple is often dried by conventional methods (e.g. hot-air drying, freeze-drying, etc.), which are usually uncontrolled and then prone to product quality deterioration. Thus, to allow the production of high-value end products, there is a need for developing new drying systems. In this study, it was investigated the feasibility of an implementation of NIR spectroscopy in smart drying systems to non-destructively detect and monitor physicochemical changes in organic apples wedges during 8-h hot-air drying at 60 °C. Moreover, the impact of microwave heating pre-treatment (at 850 W for 45 s) as enzyme inactivators on model performances was also evaluated. Partial least squares (PLS) regression models were successfully developed to monitor changes in water activity (R2 = 0.97–0.98), moisture content (R2 = 0.97–0.98), SSC (R2 = 0.96–0.97) and chroma (R2 = 0.77–0.86) during drying. Classification analysis was performed for the development of discriminant models able to recognise dehydration phases of apple wedges on the basis of their spectral profile. The classification models were computed using K-means and Partial Least Squares Discriminant Analysis (PLS-DA) algorithms in sequence. The performance of each PLS-DA model was defined based on its accuracy, sensitivity and specificity rates. All of the selected models provided a very-good (>0.90) or excellent (>0.95) sensitivity and specificity rates for the predefined drying phases. Feature selection procedures allowed to obtain both regression and classification models with performances very similar to models computed from the full spectrum. Results suggest that effect of microwave heating on both water loss and microstructure of apple tissue was pronounced, mainly affecting the features selection procedure in terms of number of features and selected wavelengths.

Quality of a product and sustainability of its production depend on the cumulative impacts of each processing step in the food chain and their interplay. Various research studies evidenced that many drying systems operate inefficiently in terms of drying time, energy demand (e.g. fossil fuels), raw material utilisation and resulting product quality. Moreover, not all conventional drying processes are allowed in the organic sector (Reg. EC 834/2007; Reg. EC 889/2008). In recent years, non-invasive monitoring and control systems have shown a great potential for improvement of the quality of the resulting products. Thus, there is a need for smart processes which allow for simultaneous multi factorial control to guarantee high-value end products, enhance energy and resource efficiency by using innovative and reliable microcontrollers, sensors and embracing various R&D areas (e.g. computer vision, deep learning, etc.). The objective of this study was to evaluate the feasibility of computer vision (CV) as a tool in development of smart drying technologies to non-destructively forecast changes in moisture content of apple slices during drying. Usage of computer vision (CV) as Process Analytical Technology in drying of apple slices was tested. Samples were subjected to various anti-browning treatments at sub- and atmospheric pressures, and dried at 60°C up to a moisture content on dry basis (MCdb) of 0.18 g/g. CV-based prediction models of changes in moisture content on wet basis (MCwb) were developed and promising results were obtained (R2P > 0.99, RMSEP = 0.011÷0.058 and BIASP < 0.06 in absolute value), regardless of the anti-browning treatment. The proposed methodology lays the foundations for a scale-up smart-drying system based on CV and automation.

Context: Several urinary PAHs metabolites can be detected by HPLC-MS/MS for individual exposure assessment.Objective: Quantitation of urinary metabolites of four PAHs, selected on the basis of their significance, with reduced costs and high sensitivity.Materials and methods: HPLC-MS/MS was used and pure standards and isotope-labeled internal analogs of the analytes. Two hundred samples were tested after enzymatic hydrolysis.Results: Accuracy was higher than 90% and variability lower than 19%; LODs permit to measure 1-hydroxypyrene, 1 and 2-hydroxynaphthalene in all subjects, 6-hydroxynitropyrene in the 65% and 3-hydroxybenzo[a]pyrene in the 70%.Discussion and conclusion: The method is suitable both for occupational and for environmental studies. This is the first paper reporting urinary levels of 6-hydroxynitropyrene in European subjects, nonoccupationally exposed to nitro-PAHs.

This paper presents a machine vision retrofit system designed for upgrading used tractors to allow the control of the tillage implements and enable real-time field operation. The retrofit package comprises an acquisition system placed in the cabin, a front-mounted RGB camera sensor, and a rear-mounted Peiseler encoder wheel. The method combines shape analysis and colorimetric k-nearest neighbor (k-NN) clustering for in-field weed discrimination. This low-cost retrofit package can use interchangeable sensors, supplying flexibility of use with different farming implements. Field tests were conducted within lettuce and broccoli crops to develop the image analysis system for the autonomous control of an intra-row hoeing implement. The performance showed by the system in the trials was judged in terms of accuracy and speed. The system was capable of discriminating weed plants from crop with few errors, achieving a fairly high performance, given the severe degree of weed infestation encountered. The actuation time for image processing, currently implemented in MATLAB integrated with the retrofit kit, was about 7 s. The correct detection rate was higher for lettuce (from 69% to 96%) than for broccoli (from 65% to 79%), also considering the negative effect of shadows. To be implementable, the experimental code needs to be optimized to reduce acquisition and processing times. A software utility was developed in Java to reach a processing time of two images per second.

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.

The worldwide consumption of dried carrot (Daucus carota L.) is on a growing trend. Conventional methods for drying carrots include hot-water blanching followed by hot-air drying, which is usually uncontrolled and therefore prone to product quality deterioration. Thus, there is a need for innovative drying systems that yield high-value end products. In this study, the efficacy of NIR spectroscopy for the non-destructive monitoring of physicochemical changes and drying behaviour in organic carrot slices during 8-h hot-air drying at 40 °C was demonstrated using Partial least squares (PLS) regression and PLS discriminant analysis (PLS-DA). The impact of hot-water blanching pre-treatment (at 95 °C for 1.45 min) for enzyme inactivation on performances of both regression and classification models was also evaluated. PLS regression models were successfully developed to monitor changes in water activity (R2 = 0.91–0.96), moisture content (R2 = 0.97–0.98), total carotenoids content (R2 = 0.92–0.96), lightness for unblanched carrots (R2 = 0.80–0.83) and hue angle for blanched samples (R2 = 0.85–0.87). Soluble solids content prediction was poor for both treatments (RMSEP = 3.43–4.40). Classification models were developed to recognise dehydration phases of carrot slices on the basis of their NIR spectral profile using K-means and PLS-DA algorithms in sequence. The performance of each PLS-DA model was defined based on its accuracy, sensitivity and specificity rates. All of the selected models provided from good (> 0.85) to excellent (> 0.95) sensitivity and specificity for the predefined drying phases. Feature selection procedures yielded both regression and classification models with performances very similar to models computed from the full spectrum. Moscetti, R., Haff, R.P., Ferri, S.et al.Real-Time Monitoring of Organic Carrot (var. Romance) During Hot-Air Drying Using Near-Infrared Spectroscopy.Food Bioprocess Technol10,2046–2059 (2017). https://doi.org/10.1007/s11947-017-1975-3 &nbsp

Introduction: exposure assessment of residents in industrial areas is a crucial issue in environmental epidemiology . We propose a novel analytical method for simultaneous quantification of urinary...

An overview is given regarding the most recent use of non-destructive techniques during drying used to monitor quality changes in fruits and vegetables. Quality changes were commonly investigated in order to improve the sensory properties (i.e., appearance, texture, flavor and aroma), nutritive values, chemical constituents and mechanical properties of drying products. The application of single-point spectroscopy coupled with drying was discussed by virtue of its potentiality to improve the overall efficiency of the process. With a similar purpose, the implementation of a machine vision (MV) system used to inspect foods during drying was investigated; MV, indeed, can easily monitor physical changes (e.g., color, size, texture and shape) in fruits and vegetables during the drying process. Hyperspectral imaging spectroscopy is a sophisticated technology since it is able to combine the advantages of spectroscopy and machine vision. As a consequence, its application to drying of fruits and vegetables was reviewed. Finally, attention was focused on the implementation of sensors in an on-line process based on the technologies mentioned above. This is a necessary step in order to turn the conventional dryer into a smart dryer, which is a more sustainable way to produce high quality dried fruits and vegetables.