Feasibility of Vis/NIR spectroscopy and image analysis as basis of the development of smart-drying technologies

[EN] Drying is a complex, dynamic, unsteady and nonlinear process that, when not optimized on a system level, may be responsible for (1) significant quality degradation and (2) energy wastage. Consequently, new drying technologies must be designed combining non-invasive at-/on-/in-line advanced measurement and control systems with models cross-linking all relevant aspects of product quality changes and heat and mass transfer phenomena. This paper presents preliminary results on the use of RGB imaging, NIR spectroscopy and Vis-NIR hyperspectral imaging for real-time monitoring of physicochemical changes of apples and carrots during drying.The authors gratefully acknowledge CORE Organic Plus for financial support through the SusOrganic project titled: ‘Development of quality standards and optimized processing methods for organic produce’ (Nr: 2814OE006).Sturm, B.; Moscetti, R.; Crichton, S.; Raut, S.; Bantle, M.; Massantini, R. (2018). Feasibility of Vis/NIR spectroscopy and image analysis as basis of the development of smart-drying technologies. En IDS 2018. 21st International Drying Symposium Proceedings. Editorial Universitat Politècnica de València. 171-178. https://doi.org/10.4995/IDS2018.2018.7616OCS17117

Moisture content measurement in dried apple produce through visible wavelength hyperspectral imaging

Traditional fruit drying within industry is undertaken using tried, tested and trusted drying schemes for different food produce. This maximises yield for a specific output food quality when input food quality is strictly controlled. However this strict control of input food quality leads to excess food wastage at the input stage. The use of set drying treatments can also result in higher energy usage during the drying process than may be necessary. In order to investigate methods to reduce both food wastage and energy usage we propose the tailoring of the drying process to the incoming food quality and condition.To achieve this we investigate the feasibility of online moisture content estimation of apple discs during the drying process through the use of hyperspectral imaging in the visible range (400-780nm). This involved the drying of apple samples, Braeburn variety, within a general purpose oven, Genlab J-SS-OH, at 70°C. Imaging was undertaken using a Specim V10E hyperspectral camera with a rotational scanning mirror attachment and Schneider Xenoplan 50mm f/2.8 lens under illumination provided by a Verivide D65 ‘Artificial Daylight’ lamp (CCT 6500K), in a viewing cabinet. 54 Braeburn apples, at the same ripeness stage, were sliced into 5mm thick discs of uniform size and inner and outer diameters. These were then dried within the oven at 70°C for a period of 6 hours. Each disc was individually hyperspectrally imaged at 1 hour intervals in conjunction with mass measurement of each apple disc. From regression analysis, a good level of moisture content prediction within this wavelength region was uncovered

Impact of bulk weight on drying behaviour and hop quality after drying

Hops are a key ingredient for beer brewing due to their role in the creation of the foam characteristics, bitterness of the beers and aroma. Whilst in the past foam and bitterness were the key characteristics sought by the market, the last decade has seen a steep increase in demand of aroma hops for the production of crafts beers. Color of the final product plays a major role in quality perception of traders and brewers. Therefore, color changes were investigated to estimate the impact of bulk weight and thus drying time and conditions on the upper surface of the bulk. A calibrated imaging system consisting of a CCD camera and illumination was integrated into the dryer. Further, changes of and ß acid contents were investigated. Hops of the variety Mandarina Bavaria were dried at 65°C and an air velocity of 0.35 m/s. Bulk weights investigated were 12, 20 and 40 kg/m² respectively. Drying times were 105, 135, and 195 min. Drying characteristics showed a unique development which very likely is due to the distinct physiology of hop cones (string, bracteole, bract, lupilin glands). Color changes depended strongly on the bulk weight and resulting bulk thickness whilst a and ß acid contents were not affected by the drying conditions. The research presented showed that air mass flow in relation to the mass of water to be removed is critical for the quality of the product as well as the processing time required

Measurement of hop moisture content and chromaticity during drying with VNIR hyperspectral imaging

The drying of hops is a crucial post harvesting stage in the production of beer. If hops are not dried to below a specific moisture content they will spoil prior to being processed into pellets for beer production. Further to this, drying of hops is usually undertaken by farmers themselves, and with a single harvest per year the drying operation is of great economic importance for their survival. The monitoring of moisture content is usually undertaken through moisture and humidity sensors placed within the hops themselves. However this method leads to the sampling of moisture content in specific spots, and as such relies upon drying uniformity. Furthermore the moisture content of the hops at the input stage varies greatly with environmental conditions. Optical methods with 2D sensors offer the opportunity to monitor moisture content changes across the entire drying area. With hyperspectral imaging allowing investigations to uncover the most important wavelengths related to moisture content. To investigate this Mandarina Bavaria hops were imaged during the drying process in-situ using a hyperspectral camera (Specim PFD-V10E) across the 400-1000nm region. Drying was undertaken at two temperatures (65, 70°C), with three different bulk weights (12, 20 and 40 kg/m²) and at two air speeds (0.35 and 0.50-0.35m/s). This was to introduce variation into the model to allow fully characterisation of spectral changes of hops during drying. Investigations have shown that a simple optical system using a small number of wavelengths can be used to estimate hop moisture content and chromaticity

Use of hyperspectral imaging for the prediction of moisture content and chromaticity of raw and pretreated apple slices during convection drying

The feasibility of using spectral reflectance information in the visible—near infrared (400–1,000 nm) region to estimate moisture content (gW/gDM) and chromaticity (CIELAB) of apple slices was investigated during convection drying. Apple slices were pretreated with hot water blanching (50 and 70°C), acid application (citric and ascorbic), and combinations thereof before drying at 50 and 70°C. Prediction models for the space-averaged spectral reflectance curves were built using the partial least square regression method. A three-component partial least square regression (PLSR) model satisfied the minimal root mean square error (RMSE) criterion for predicting moisture content (avg. RMSEP = 0.13, r2 = 0.99); importantly, the critical wavelengths remained the same across all pretreatments (540, 817, 977 nm). Similarly, PLSR modeling showed that the optimal set of wavelengths (in terms of RMSE) were invariant across pretreatment for CIELAB a* prediction (543, 966 nm) and CIELAB b* prediction (510, 664, 714, 914, 969 nm). The stability of the information content of these wavelengths across pretreatments indicates their independence of color changes. Additionally, the spatial information in the hyperspectral images was exploited to visualize the performance of the predictive models by pseudo-coloring their values for each pixel in a single apple slice across different drying times. This visualization of spatial distribution of predicted moisture content and chromaticity changes shows significant potential for use in online monitoring of the drying process

Comparative analysis of methods and model prediction performance evaluation for continuous online non-invasive quality assessment during drying of apples from two cultivars

Quality attributes such as moisture content, colour parameters and shrinkage of apples change undesirably during the drying process. Drying is a highly dynamic process, thus, an effective optimisation in terms of product quality and process performance requires continuous non-invasive measurement of the parameters in question. In this study, visual to near infra-red hyperspectral imaging was used in combination with the respective gold standard methods to estimate moisture ratio, CIELab chromaticity, browning index, shrinkage, and rehydration ratio of apple slices during the hot air-drying process. Two varieties (cv. Elstar and Golden delicious) of apples at three slice thicknesses (2, 3, and 4 mm) were dried at 60 °C and 70 °C. Prediction models for the space-averaged spectral reflectance curves were built using the partial least square regression method and including both varieties. The performance of moisture ratio prediction was excellent (adj R2 = 0.94, RMSEP = 0.076) and the Variable Importance in the Projection value cut off above 0.8 at 970 nm and L* at 685 nm. Similarly, partial least square regression modelling showed a good prediction for a*, b* value, BI, shrinkage and acceptable prediction for L* and RR. The model performance was robust to the system settings irrespective of slice thickness, drying temperature and apple variety. Additionally, method comparisons using Bland-Altman, Bablok, and Deming regression were performed. The results confirm that the compared destructive (laboratory gold standard) and non-destructive hyperspectral methods can be interchangeably used within the limit of agreement (±1.96 standard deviations) and precision for determination of the MR, CIELAB chromaticity and BI, shrinkage, and rehydration ratio. Therefore, these results confirm that hyperspectral imaging system can be used in online monitoring of the apples during the drying process, and thus, in the optimisation of product and process performance quality attributes

Effect of maturation and freezing on quality and drying kinetics of beef

The quality of dried meat products and the drying kinetics significantly depends on the status of the raw material going into the drying process. The aim of this study was the determination of the impact of meat status (fresh, mature, frozen-thawed) on the drying kinetics and the resulting quality in terms of colour changes and spectrally deductible information. Drying tests were conducted using meat from organically raised bulls. In fresh meat, freezing leads to a decrease of the drying rate, whilst for matured meat the opposite is true. Aging and freezing have little effect on the end product quality in terms of final product colour. However, water content can be detected hyperspectrally and resolved spatially for all stages of the pro-cess. With regards to water content prediction, the MCUVE-PLS method performs best for the fresh and fresh frozen-thawed version with seven wavelengths, an r2 of 0.97 and 0.88, and RMSE of 0.15 and 0.17 for the test set, respectively