1,721,016 research outputs found
Inversion of a numerical model to predict the effective moisture diffusivity of fruits during drying as a function of temperature and moisture content
Full text linkDrying is one of the most important fruit and vegetable preservation method useful to remove water from fresh materials. This permits to avoid growth of bacteria, yeasts or moulds. To better understand the heat and mass transfer process that take place during drying, numerical models can be used. The effective diffusivity determination is a crucial aspect for the development of drying models. The diffusivity is affected by the product temperature and moisture content. The aim of this study was to develop a method, based on the inversion of a finite element model, to estimate the moisture diffusivity of fruits during drying as a simultaneous function of temperature and moisture content. The research work was divided in three phases: i) experimental determination of the moisture concentration versus time in various fruits during the drying process; ii) development of a numerical heat and mass transfer model for the determination of moisture content versus time; iii) parameter estimation of moisture diffusivity as a function of temperature and moisture content, by minimizing the distance between numerical model and experimental results using a feasible optimization algorithm. The estimated moisture diffusivity coefficients are close to those reported in literature for the same fruits (banana and apple), but obtained by others approaches. The experimental and calculated moisture contents are in good agreement showing a determination coefficient R2>0.97
In-field hyperspectral imaging: An overview on the ground-based applications in agriculture
Full text linkThe measurement of vegetation indexes that characterise the plants growth, assessing the fruit ripeness or detecting the presen-ce of defects and diseases, is a key factor to gain high quality of fruit or vegetables. Such evaluation can be carried out using both destructive and non destructive techniques. Among non-destructi-ve techniques, hyperspectral imaging (HSI), combining image analysis and visible/near-infrared spectroscopy, looks particularly useful. Many studies have been published concerning the use of hyperspectral cameras in the agronomic and food field, especially in controlled laboratory conditions. Conversely, few studies described the application of HSI technology directly in field, especially involving ground-based systems. Results suggest that this technique could be particularly useful, even if the role of environmental variables has to be considered (e.g., intensity and incidence of solar radiation, wind or the soil in the background). In this paper, recent in-field HSI applications based on ground systems are reviewed
Evaluation of a mobile NIR spectrometer and cloud data analysis system for food quality rapid assessment
No full textNear Infrared spectroscopy (NIR) is one of main techniques used in industry to determine food quality parameters in a rapid and nondestructive way. Recently, some simple and cheaper NIR spectrometers are becoming more common. The aim of this research was to evaluate the performance of the miniaturized NIR SCiO for the determination of quality parameters of some common foodstuff.
Vegetable (apples, peaches, pistachio and tomato paste), bakery (bread), diary (milk and cheese) and confectionary (chocolate) products were analyzed by using SCiO sensor and a common NIR tool (MATRIX TM-F, Bruker Optics). To estimate specific qualitative parameters classification and predictive statistical models were developed by using SCiO Lab tool and a commercial multivariate statistical software.
Similar results were achieved by using SCiO and classical NIR. In general, good models were obtained for all food products with determination coefficients (R2) range from 0.765 to 0.991. Considering the final consumer demands, the SCiO solution appeared a suitable instrument for a rapid evaluation of many food quality indexes
Thermophysical properties of frozen parsley: A state diagram representation
Full text linkSorption behavior and glass transition of frozen parsley were investigated in order to study the physical modifications, represented as state diagram to figure out information about product behavior for storage and supply chain handling. Frozen products may be located in the state diagram in all the possible freezing temperatures to understand the structure state and study feasible corrective actions mainly focused on temperature and solid content. Parsley was totally dehydrated and equilibrated at selected relative humidity. Sorption behavior was evaluated by saturated salt slurry method and sorption isotherm fitting, while solid components in terms of mass fraction were investigated by using differential scanning calorimetry. Brunauer–Emmet–Teller (BET) model well fitted with moisture and water activity data (R2 = 0.995, p-level < 0.05). The characteristic ranges of stability, in terms of system mobility and physical modification, are the monolayer values (0.240 water activity; 0.052 kg/kg dry basis) and the critical water activity range (0.424 water activity; 0.078 kg/kg dry basis). Glass transition and melting temperature were used to define parsley state diagram. Different zones figure out a specific physical behavior: depending on solid content and temperature. Unfrozen water was estimated as monolayer BET value 0.84 kg/kg wet basis and extrapolated from state diagram 0.984 kg/kg wet basis. Practical applications: Frozen high-quality product and energy efficiency can be obtained by a correct temperature management. Thermophysical properties of parsley represented in a state diagram are reported in the typical solute and temperature range of freezing process and storage. These data can relate physical structure and temperature, allowing different possible conditions management
Hyperspectral imaging to measure apricot attributes during storage
Full text linkThe fruit industry needs rapid and non-destructive techniques to evaluate the quality of the products in the field and during the post-harvest phase. The soluble solids content (SSC), in terms of °Brix, and the flesh firmness (FF) are typical parameters used to measure fruit quality and maturity state. Hyperspectral imaging (HSI) is a powerful technique that combines image analysis and infrared spectroscopy. This study aimed to evaluate the potential of the application of the Vis/NIR push-broom hyperspectral imaging (400 to 1000 nm) to predict the firmness and the °Brix in apricots (180 samples) during storage (11 days). Partial least squares (PLS) and artificial neural networks (ANN) were used to develop predictive models. For the PLS, R2 values (test set) up to 0.85 (RMSEP=1.64 N) and 0.72 (RMSEP=0.51 °Brix) were obtained for the FF and SSC, respectively. Concerning the ANN, the best results in the test set were achieved for the FF (R2=0.85, RMSEP=1.50 N). The study showed the potential of the HSI technique as a non-destructive tool for measuring apricot quality even along the whole supply chain
Storage of wafer cookies: Assessment by destructive techniques, and non-destructive spectral detection methods
Full text linkWafer cookies combine two or more layers of wafer sheets with intermediate layers of cream filling and later coating with chocolate. During storage, wafer cookie quality, especially in terms of mechanical properties, is mainly affected by moisture migration from the cream or chocolate and moisture absorption from air. This study aimed to assess the storage of wafer cookies by destructive (water activity, mechanical properties, and sensory acceptance) and non-destructive methods (image analysis, NIR spectroscopy and hyperspectral imaging HSI). Furthermore, two packaging types were considered. Samples were stored at 18 °C (RH = 50%) and analysed after 2, 4, 5, 6, 7 and 8 months. Good linear relations (R2 up to 0.84) were found between water activity and mechanical parameters, confirming the dependence between textural aspects and water content. By adding a multi-material packaging layer, the shelf life significantly increased in terms of sensory acceptance (crispness). No significant differences were found between the surface colour parameter (white index) attributable to fat bloom formation. PCA results of NIR and HSI spectra showed a clear separation between samples acquired at time 0 and those analysed during storage that was related with the packaging type and storage time. PLS models developed to estimate the storage time showed R2 ranging from 0.926 (RMSECV = 0.63 months) to 0.960 (RMSECV = 0.52 months), while the water activity ranged from 0.858 to 0.928 (RMSECV = 0.02 months). The PLS models based on HSI spectra were used to obtain predictive images of water activity or storage tim
In-field and non-destructive monitoring of grapes maturity by hyperspectral imaging
Full text linkMonitoring the quality attributes of grapes is a practice that allows the state of ripeness to be checked and the optimal harvest time to be identified. A non-destructive method based on hyperspectral imaging (HSI) technology was developed. Analyses were carried out directly in the field on a ‘Sangiovese’ (Vitis vinifera L.) vineyard destined for wine production, by using a Vis/NIR (400–1000 nm) hyperspectral camera. One vineyard row was analysed on 13 different days during the pre-harvest and harvest time. The soluble solids content (SSC) expressed in terms of °Brix was measured by a portable digital refractometer. Afterwards, the grape samples were split in two classes: the first one composed by the samples characterised by a °Brix lower than 20 (not-ripe), while the second one by the samples with a °Brix higher than 20 (ripe). Grape mean spectra were extracted from each hyperspectral image and used to predict the SSC by partial least squares regression (PLS), and to classify the samples into the two classes by PLS discriminant analysis (PLS-DA). SSC was predicted with a R2 = 0.77 (RMSECV = 0.79 °Brix), and the samples were correctly classified with a percentage from 86 to 91%. Even if the number of wavelengths was limited, the percentages of correctly classified samples were again within the above-mentioned range. The present study shows the potential of the use of HSI technology directly in the field by proximal measurements under natural light conditions for the prediction of the harvest time of the ‘Sangiovese’ red grape
Data fusion of FT-NIR spectroscopy and Vis/NIR hyperspectral imaging to predict quality parameters of yellow flesh “Jintao” kiwifruit
Full text linkThe internal quality of kiwifruit, in terms of soluble solid content (SSC), flesh firmness (FF), and dry matter (DM), is widely recognised as a key feature for fruit sorting and pre-harvest assessment. Furthermore, flesh hue (FH) is another important parameter to consider for yellow flesh kiwifruits. NIR and VIS/NIR spectroscopic techniques are valuable alternatives for rapid and non-destructively prediction of all these quality parameters in fruit. Accordingly, the aim of this work was to build a partial least square (PLS) regression models to estimate SSC, FF, FH, and DM of yellow fleshed Actinida chinensis (Jintao) starting from Vis/NIR hyperspectral imaging (400–1000 nm) and FT-NIR (800–2500 nm) spectroscopy data. To take advantage of the complementary information of the two different spectral ranges, data fusion strategies were investigated to concatenate the data before PLS models. In particular, two different sequential fusion methods were used: low-level data fusion based on the concatenation of the pretreated spectra, and mid-level feature fusion characterised by the concatenation of features (scores) obtained by principal component analysis (PCA) or PLS models developed considering individually each data set. For all quality parameters, the best results were achieved by adopting the second approach of mid-level data fusion (PLS scores), reporting (test set validation) of 0.914 (RMSEP=0.97°Brix), 0.843 (RMEP=1.82°H), 0.866 (RMSEP=9.41N), and 0.854 (RMSEP = 0.64%) for SSC, FH, FF, and DM, respectively. Furthermore, with respect to the PLS models from the individual data sets, the results reported a mean RMSEP reduction of 16.0 ± 4.8%, confirming the potential of the data fusion in improving the PLS prediction power for the quality parameter of kiwifruit
Estimation of the effective moisture diffusivity in cake baking by the inversion of a finite element model
Full text linkThe moisture diffusivity of food is a very important physical parameter to model baking processes. Unfortunately, specific moisture diffusivity values are not easily found in literature, especially measured or calculated during the baking processes. The main methods used to estimate the moisture diffusivity are based on the second Fick's law, but there are significant differences in the way of applying these laws. The aim of this work is to estimate the effective moisture diffusivity in baking of a small flat cake by using the inversion of a numerical model based on the coupling of heat and mass transfer. The temperature and moisture dependency of the diffusivity is evaluated. Results are compared with those obtained by using a fitting method. The inverse method allows to estimate the effective moisture diffusivity close to those reported in literature for similar bakery products (constant values). The impact of the moisture concentration appears to be very restricted and it tends to decrease with increasing the oven temperature. Subsequently, the obtained effective diffusivity was implemented on a direct Finite Element (FE) model simulating a whole cake for different baking temperatures. The direct model validation (mean moisture content and temperature) shows determination coefficients ranging from 0.921 to 0.996 confirming the robustness of the diffusivity parameters obtained by the inverse method
Physical Stability of Frozen Eggplant: Emphasis on State Diagram, Sorption, Thermal, Mechanical, and Dielectric Properties
Full text linkThe frozen eggplant was studied using absorption isotherms, measured by using saturated salt solution in desiccator chamber (DES) and the Dynamic Vapor Sorption (DVS) instrument. Melting temperature and glass transition temperature were determined by differential scanning calorimetry (DSC), while mechanical properties were determined by a compression test. In addition, dielectric properties were assessed by means of two instrumental chains to cover a wide frequency range of radiofrequencies. Absorption fitting was able to estimate unfreezeable water content, while the dynamic instrument showed a hysteresis between adsorption and desorption, confirming amorphous materials' presence in the products. Thermograms revealed two phase transition apparent T-g(III) and T-g(II) affected by the plasticizing effect of water. Mechanical properties confirmed the water influence on structures, as Fermi model fitting (R-2 = 0.984, RMSE = 3.9 N) shown. Dielectric properties were carried out to allow the description of three main dispersion alpha, beta, and gamma relaxations. State diagram was developed to show different zones corresponding to possible physical structures
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