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Ready-to-eat foods that does not receive a heat treatment before being consumed can be at risk of foodborne hazards and spoilage, so it would be of great interest to have a method for monitoring their safety. This work expands on and enhances previous successfully studies with hyperspectral imaging in the SW-NIR range. Specifically, a k-nearest-neighbours model was developed to classify the salmon tissue into white myocommata stripes (fat) and muscle (lean) tissue. Partial Least Squares models developed confirm that a spatial segmentation should be performed before a shelf life model can be calculated. Employing the fat spectra and only the 7 most correlated wavelengths, a support vector machine model was calculated to classify into days 0, 10, 20, 40 and 60 with 87.2% prediction accuracy. These results make the method developed very promising as a non-destructive method to analyse the shelf life of vacuum-packed chilled smoked salmon fillets. [ABSTRACT FROM AUTHOR]

The bread-making process is a set of operations where could be relevant to use monitoring methods. Specifically, the fermentation phase is a crucial step in which the quality of the product can be affected. Several methods have been developed to monitor this stage based on different technologies. The aim of this study was to analyze and obtain information about the internal structure of bread dough during the fermentation process using a 3D vision system based on Structured Light (SL). The differences about fermentation behavior of two wheat flours classified as “high strength flour” by company providing were studied. The parameters of the internal structure of the baked product (final bubble size and their population density) were analyzed with 2D image segmentation. An important correlation ( r = 0.865) was observed between the 3D and 2D information, specifically between the transversal area and height (3D), and final bubble size and number of bubbles (2D). Although at the end of the dough fermentation process ( Tf ) the area ( A ) and maximum height ( H ) were different, the relationship between both parameters were similar, reaching a similar bubble size as a consequence of coalescence phenomena, independent of the bubble growth rate. This could be a base for the development of prediction models and devices to monitor the fermentation phase of the bread-making process. [ABSTRACT FROM AUTHOR]

Highlights: [•] A 3D vision technique for continuous monitoring of dough fermentation is proposed. [•] Ten samples of wheat flour with the same physicochemical properties were analyzed. [•] Wheat flour samples present different behavior during fermentation process. [•] The information obtained by technique is able to characterize the flour behaviors. [Copyright &y& Elsevier]

Abstract: A comparison between the techniques Time of flight (TOF) and Structured Light (SL), for in-line process, was carried out to determine the volume of several food types with different compositions, structures and dimensions; 2 meat tissue (lean meat and pork fat) and 3 vegetable tissue (potato, apple and avocado). The volume obtained was compared with that determined by physical measurements, employing two statistical methods (a Bland–Altman study and partial least square analysis). Results showed that Structured Light (SL) was a better technique than Time of flight (TOF) for determining volume. The TOF technique was affected by factors which were more influential when the S/V ratio (surface of the sample exposed to the camera and sample volume) increased. SL was slightly affected by the composition of the sample. Fat content and the level of unsaturated fatty acids could be the reason for the reflection of the laser light on the surface of the samples thereby reducing the accuracy. Even so, the values of R 2 and RMSE of cross-validation, for the worst fit, demonstrated the quality of the SL technique. [Copyright &y& Elsevier]

Highlights: [•] Hyperspectral information in the range of SW-NIR used to detect expired salmon. [•] Technique used does not require manipulating the packed salmon samples. [•] Discrimination of fish tissues using quartile segmentation in RGB images. [•] Comparative of our method to filter hyperspectral information with classic filters. [Copyright &y& Elsevier]

A hyperspectral image analysis was used to characterize heat treatment in oat flour, performed by treating oat flour at 80, 100 and 130 °C for 30 min. Images from both original oat and treated flours were captured, and hyperspectral information was collected. Oat flours were used to obtain composite flours based on two different substitution levels (10 and 20%) of wheat flour. Composite breads were produced from the obtained flours. A battery of analyses was run to characterize them in terms of physical properties. The hyperspectral information of oat flours was analyzed by multivariate statistics and a pattern evolution-depending temperature was observed. Similarly, a set of the physical properties of breads was analyzed based on multivariate statistics, and a pattern of temperature-dependent evolution, in addition to the substitution level, was also recognized. Multivariate non linear regressions were done between both data sets to study their relationship and high values in the calibration and cross-validation results obtained. The changes undergone by oat flour during treatment were characterized with hyperspectral information, which could represent a non destructive monitoring tool to then regulate it until oat flours are obtained that confer composite bread adequate properties. [ABSTRACT FROM AUTHOR]

The objective of this study was to test the capability of a SW-NIR hyperspectral image technique to detect adulterations in wheat flour and bread with cheap grains, such us sorghum, oats and corn, and to compare the hyperspectral information with the physicochemical alterations in the properties of products. Wheat flour was adulterated at four different degrees (2.5, 5, 7.5 and 10%) with sorghum, oat and corn flours. Flours were prepared and used to make bread. Flours and breads were characterized according to several physicochemical parameters (pasting properties, water activity, mass loss during the baking process and texture profile analysis). Crumbs were extracted from breads and conditioned. Hyperspectral image captures were taken of both flours and conditioned crumbs. The data analysis was based on multivariate statistical process control method (MSPC), where the differentiation of adulterated samples was observed in all cases for both flours and crumbs. Finally, in order to relate the image analysis results and the adulterated sample properties, a correlation significance map was created between the physicochemical properties of samples and the multivariate statistical parameters. The SW-NIR image technique was capable of detecting adulterations in each case and high correlation significances were observed (α = 0.01) between wavelengths from specific spectra zones and the physicochemical properties of samples. [ABSTRACT FROM AUTHOR]