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Pineapple shell, considered a waste in the juice industry, was used as a reinforcement material to produce biodegradable foam trays (FTs) based on cassava starch by a compression molding process. These foams were prepared with different starch/fiber ratios and then were characterized according to their microstructure and physical and mechanical properties. The starch/fiber ratio of 95/5 showed the lowest values of thickness and density (2.58 mm and 367 kg m−3, respectively). There was a good distribution of the pineapple shell fiber throughout the polymeric matrix. All FTs showed a semicrystalline structure and 95/5 ratio showed the highest crystallinity index (CI) value (39%). In addition, this ratio improved the tensile strength of the FTs, obtaining similar values to expanded polystyrene (EPS) samples, used as the reference material. Nevertheless, all FTs reinforced with pineapple shell fiber showed high water absorption capacity (WAC); therefore, future studies should focus on to improve the physicochemical and structural properties of the cassava starch-based foams, considering the promising potential of this novel biodegradable material for dry food packaging, such as a viable alternative to reduce the use of petroleum-based materials such as commercial EPS trays. [ABSTRACT FROM AUTHOR]

Cinnamon is a valuable aromatic spice widely used in pharmaceutical and food industry. Commonly, two-cinnamon species are available in the market, Cinnamomum verum (true cinnamon), cropped only in Sri Lanka, and Cinnamomum cassia (false cinnamon), cropped in different geographical origins. Thus, this work aimed to develop classification models based on NIR-hyperspectral imaging (NIR-HSI) coupled to chemometrics to classify C. verum and C. cassia sticks. First, principal component analysis (PCA) was applied to explore hyperspectral images. Scores surface displayed the high similarity between species supported by comparable macronutrient concentration. PC3 allowed better class differentiation compared to PC1 and PC2, with loadings exhibiting peaks related to phenolics/aromatics compounds, such as coumarin (C. cassia) or catechin (C. verum). Partial least square discriminant analysis (PLS-DA) and Support vector machine (SVM) reached similar performance to classify samples according to origin, with error = 3.3 % and accuracy = 96.7 %. A permutation test with p < 0.05 validated PLS-DA predictions have real spectral data dependency, and they are not result of chance. Pixel-wise (approach A) and sample-wise (approach B, C and D) classification maps reached a correct classification rate (CCR) of 98.3 % for C. verum and 100 % for C. cassia. NIR-HSI supported by classification chemometrics tools can be used as reliable analytical method for cinnamon authentication., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Large amount of information in hyperspectral images (HSI) generally makes their analysis (e.g., principal component analysis, PCA) time consuming and often requires a lot of random access memory (RAM) and high computing power. This is particularly problematic for analysis of large images, containing millions of pixels, which can be created by augmenting series of single images (e.g., in time series analysis). This tutorial explores how data reduction can be used to analyze time series hyperspectral images much faster without losing crucial analytical information. Two of the most common data reduction methods have been chosen from the recent research. The first one uses a simple randomization method called randomized sub-sampling PCA (RSPCA). The second implies a more robust randomization method based on local-rank approximations (rPCA). This manuscript exposes the major benefits and drawbacks of both methods with the spirit of being as didactical as possible for a reader. A comprehensive comparison is made considering the amount of information retained by the PCA models at different compression degrees and the performance time. Extrapolation is also made to the case where the effect of time and any other factor are to be studied simultaneously., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

Food fraud in herbs and spices is an important topic, which has led to new technologies being studied as potential tools for fraud identification. Nontargeted technologies have proven to be a useful tool for the authentication of herbs and spices. The present review focuses on the use of near-infrared, hyperspectral imaging, Fourier-transform infrared, Raman, nuclear magnetic resonance, and electron spin resonance spectroscopy for the authentication of spices, which includes the determination of origin and irradiated spices and the identification of adulterants. The methods developed based on vibrational spectroscopy combined with chemometric techniques seem to be promising tools for determining the presence of adulterants and contaminants in herbs and spices. On the other hand, nuclear magnetic resonance seems to be the most efficient technology to determine the origin of herbs and spices although, for some cases, studies with near-infrared spectroscopy can be a viable substitute. Electron spin resonance spectroscopy is the technique par excellence used for the authentication of irradiated herbs and spices, so its use should be expanded to many more spices' varieties. Portable devices are preferred by those involved in the food industry, due to its manageability and low cost. Data fusion and big data are shown as promising tools for spice fraud control. In conclusion, spectroscopic techniques show a great efficiency to authenticate spices, although their evaluation must be expanded to other spice varieties, to new strategies of data analysis (as data fusion and big data), and to the use of portable devices., (© 2019 Institute of Food Technologists®.)

Starch was isolated from three Andean-native crops - arracacha (Arracacia xanthorrhiza), oca (Oxalis tuberosa), and sweet potato (Ipomoea batatas) - for use as a raw material for the production of foam trays. The starches were characterized in their proximal composition, crystallinity, microstructure and thermal properties. The sweet potato starch showed the highest amylose content (42.65%) and the lowest protein content (0.30%). The oca starch granules were larger (10-30 μm) than sweet potato and arracacha starch. The highest crystallinity of sweet potato starch caused larger values of onset temperature (To), peak temperature (Tp), conclusion temperature (Tc) (67.64 °C, 72.83 °C, and 81.20 °C, respectively) than arracacha and oca starch. The novel foam trays showed good appearance, adequate expansion, and low density; however, all foam trays showed a water absorption capacity >50%, which was related to their porosity and low density. Also, sweet potato and oca starch trays showed high tensile strength (0.67 and 0.65 MPa, respectively) compared with arracacha starch trays (0.52 MPa)., (Copyright © 2018 Elsevier B.V. All rights reserved.)

This work studies the adsorption of colored compounds in cane juice using a food-grade macroporous adsorber resin without functional groups. The adsorption equilibrium was studied through the adsorption isotherms at 30, 40, and 50 ℃. The absorbance at 420 nm was used to measure the concentration of colored compounds, which enables correlation of the residual concentration with the adsorbed concentration. Furthermore, the efficiency of the adsorption process was studied, from which it was observed that there was an improvement in efficiency with increasing resin content, while the increase in temperature was less important in the process. The kinetic study was performed using the Ibarz model and intraparticle diffusion model, which correctly account for the kinetics of the adsorption process. The adsorption kinetic constant was always greater than the desorption kinetic constant, indicating that the adsorption step predominates over the desorption step.

In this work the effects of pre-drying and frying time on colour, oil, texture and sensorial acceptability (overall liking) of potato chips were evaluated. Potato chips were pre-dried for 0, 10, 20 and 30 min at 60 °C and fried in soybean oil at 190 °C for 60, 70 and 80 s. The colour parameters (L*, a* and b*) increased or decreased depending on the pre-drying and frying time. Hardness increased as the pre-drying and frying time increased. On the other hand, the water initially removed by pre-drying decrease the gradient of mass transfer (water-oil). The oil content reduced to (about 21%) in pre-dried samples when compared to control sample. Finally, sensorial evaluation showed that samples without pre-drying and/or fried for very short or very long times had low acceptance levels. The pre-drying and frying times influenced the colour, texture, water and oil content, and resulted into fried potato chips with better acceptance scores.

The aim of this study was to develop a system to classify the marbling of beef using the hyperspectral imaging technology. The Japanese standard classification of the degree of marbling of beef was used as reference and twelve standards were digitized to obtain the parameters of shape and spatial distribution of marbling of each class. A total of 35 samples M. longissmus dorsi muscle were scanned by the hyperspectral imaging system of 400-1000 nm in reflectance mode. The wavelength of 528nm was selected to segment the sample and the background, and 440nm was used for classified the samples. Processing algorithms on image, based on decision tree method, were used in the region of interest obtaining a classification error of 0.08% in the building stage. The results showed that the proposed technique has a great potential, as a non-destructive and fast technique, that can be used to classify beef with respect to the degree of marbling., (Copyright © 2017 Elsevier Ltd. All rights reserved.)