Optimization and modeling of vacuum impregnation of pineapple rings and comparison with osmotic dehydration.

The vacuum impregnation (VI) process parameters (vacuum pressure = 20-60 kPa; VI temperature = 35-55°C; concentration of the sucrose solution = 40-60 °Brix; and vacuum process time = 8-24 min) for pineapple rings were optimized based on the moisture content (MC), water loss (WL), solids gain (SG), yellowness index (YI), and total soluble solids (TSS) content of pineapple rings using response surface methodology (RSM). A relationship was developed between the process and response variables using RSM and artificial neural network (ANN) techniques. The effectiveness of VI was evaluated by comparing it with the osmotic dehydration (OD) technique. The optimum condition was found to be 31.782 kPa vacuum pressure, 50.441°C solution temperature, and 60 °Brix sucrose concentration for 20.068 min to attain maximum TSS, YI, SG, and WL, and minimum MC of pineapple rings. The R2 values of RSM models for all variables varied between 0.70 and 0.91, whereas mean square error values varied between 0.76 and 71.58 and for ANN models varied between 0.87-0.93 and 0.53-193.78, respectively. Scanning electron micrographs (SEM) revealed that parenchymal cell rupture was less in VI than in OD. The VI pineapple rings exhibited more pores and high SG, as compared to OD, due to the pressure impregnation. Spectroscopic analysis affirmed that the stretching vibrations of intermolecular and intramolecular interactions were significant in VI as against OD. The VI reduced the drying time by 35% compared to OD, with the highest overall acceptability score and lower microbial load during storage. PRACTICAL APPLICATION: Pineapple is a perishable fruit, which necessitates processing for extended shelf life. This study highlights the potential of the vacuum impregnation process as a promising alternative to conventional preservation methods such as osmotic dehydration for pineapples.

Emerging nondestructive techniques to quantify the textural properties of food: A state-of-art review.

Texture is an important sensory attribute that drives consumer acceptance of any food material. In recent times consumers' demand for high-quality food urges food industries to provide food with consistent textural properties. However, texture measurement not just requires a trained sensory panel but also a considerable amount of time and effort. On the flip side, human observation could be subjective hence repeatability of the result may not be ensured and/or relied on. Contrary to that, objective methods for texture measurement are reliable and consistent, but are not suitable for in-line application and also destructive in nature. The mentioned crisis has made industries opt for nondestructive texture analysis techniques. In the past decade, considerable research has been carried out on nondestructive texture analysis methods such as micro-deformation, and acoustic and optical techniques, showing feasibility for in-line applications. The current review focuses on the working principles and most recent applications of nondestructive techniques for texture analysis of food products. Moreover, a detailed review of contact and noncontact-type texture measurement has been presented in this article. The literature survey is concluded with future research aspects and challenges involved in the commercialization of the nondestructive texture analysis techniques.

Advanced techniques in edible oil authentication: A systematic review and critical analysis.

Adulteration of edible substances is a potent contemporary food safety issue. Perhaps the overt concern derives from the fact that adulterants pose serious ill effects on human health. Edible oils are one of the most adulterated food products. Perpetrators are adopting ways and means that effectively masks the presence of the adulterants from human organoleptic limits and traditional oil adulteration detection techniques. This review embodies a detailed account of chemical, biosensors, chromatography, spectroscopy, differential scanning calorimetry, non-thermal plasma, dielectric spectroscopy research carried out in the area of falsification assessment of edible oils for the past three decades and a collection of patented oil adulteration detection techniques. The detection techniques reviewed have some advantages and certain limitations, chemical tests are simple; biosensors and nuclear magnetic resonance are rapid but have a low sensitivity; chromatography and spectroscopy are highly accurate with a deterring price tag; dielectric spectroscopy is rapid can be portable and has on-line compatibility; however, the results are susceptible to variation of electric current frequency and intrinsic factors (moisture, temperature, structural composition). This review paper can be useful for scientists or for knowledge seekers eager to be abreast with edible oil adulteration detection techniques.

Design consideration and modelling studies of ultrasound and ultraviolet combined approach for shelf-life enhancement of pine apple juice.

Although both ultraviolet (UV) radiation and ultrasound (US) treatment have their capabilities in microbial inactivation, applying any one method alone may require a high dose for complete inactivation, which may affect the sensory and nutritional properties of pineapple juice. Hence, this study was intended to analyse and optimise the effect of combined US and UV treatments on microbial inactivation without affecting the selected quality parameters of pineapple juice. US treatment (33 kHz) was done at three different time intervals, viz. 10 min, 20 min and 30 min., after which, juice samples were subjected to UV treatment for 10 min at three UV dosage levels, viz. 1 J/cm2, 1.3 J/cm2, and 1.6 J/cm2. The samples were evaluated for total colour difference, pH, total soluble solids (TSS), titrable acidity (TA), and ascorbic acid content; total bacterial count and total yeast count; and the standardization of process parameters was done using Response Surface Methodology and Artificial Neural Network. The results showed that the individual, as well as combined treatments, did not significantly impact the physicochemical properties while retaining the quality characteristics. It was observed that combined treatment resulted in 5 log cycle reduction in bacterial and yeast populations while the individual treatment failed. From the optimization studies, it was found that combined US and UV treatments with 22.95 min and1.577 J/cm2 ensured a microbiologically safe product while retaining organoleptic quality close to that of fresh juice.

Non-destructive assessment of quality parameters of white button mushrooms (Agaricus bisporus) using image processing techniques.

Considering that appearance of white button mushroom (WBM) as the trigger for registering its quality, this study was aimed at analyzing the visual cues by the application of image processing tools. While L-a-b colour space and skewness was used for estimating chromatic and morphological characteristics; onset of discolouration of WBM was predicted by hyperspectral image analysis. Undamaged (UD) and damaged (D) mushrooms were stored under refrigerated conditions (3-5 °C and 90% Rh). RGB and hyperspectral images were acquired on alternate storage days 1, 3, 5, 7 and 9. Weight loss, texture and moisture content of stored mushrooms were also recorded during the storage period. Colour changes in stored UD and D were found to be in b (21.55) and a (2399) value, respectively. Browning index in D was 83-212% higher than UD mushrooms across the storage period. Weight and firmness losses in D were higher by 65.9 and 31.4%, respectively than UD. Morphological characteristic in terms of aspect ratio and roundness were not found to vary significantly over the storage period for both UD and D mushrooms. Chemometrics revealed that multiplicative scatter correction was the best pre-processing tool and that onset on discolouration is conspicuous in the spectral region of 520-800 nm. k-NN fared better than PLS-DA for correct classification (100%) of UD and D mushrooms.

Non-destructive classification and prediction of aflatoxin-B1 concentration in maize kernels using Vis-NIR (400-1000 nm) hyperspectral imaging.

Aflatoxin-B1 contamination in maize is a major food safety issue across the world. Conventional detection technique of toxins requires highly skilled technicians and is time-consuming. Application of appropriate chemometrics along with hyperspectral imaging (HSI) can identify aflatoxin-B1 infected maize kernels. Present study was undertaken to classify 240 maize kernels inoculated with six different concentrations (25, 40, 70, 200, 300 and 500 ppb) of aflatoxin-B1 by using Vis-NIR HSI. The reflectance spectral data were pre-processed (multiplicative scatter correction (MSC), standard normal variate (SNV), Savitsky-Golay smoothing and their combinations) and classified using partial least square discriminant analysis (PLS-DA) and k-nearest neighbour (k-NN). PLS model was also developed to predict the concentration of aflatoxin-B1in naturally contaminated maize kernels inoculated with Aspergillus flavus. The potential wavelength (508 nm) was selected based on principal component analysis (PCA) loadings to distinguish between sterile and infected maize kernels. PCA score plots revealed a distinct separation of low contaminated samples (25, 40 and 70 ppb) from highly contaminated samples (200, 300 and 500 ppb) without any overlapping of data. The maximum classification accuracy of 94.7% was obtained using PLS-DA with SNV pre-processed data. Across all the combinations of pre-processing and classification models, the best efficiency (98.2%) was exhibited by k-NN model with raw data. The developed PLS model depicted good prediction accuracy ( R CV 2 = 0.820, SECV = 79.425, RPDCV = 2.382) during Venetian-blinds cross-validation. The results of pixel-wise classification (k-NN) and concentration distribution maps (PLS with raw spectra) were quite close to the result obtained by reference method (HPLC analysis) of aflatoxin-B1 detection.

Chemometric strategies for nondestructive and rapid assessment of nitrate content in harvested spinach using Vis-NIR spectroscopy.

The overuse of nitrogenous fertilizers leads to an increase in the nitrate content of green leafy vegetables. Consumption of food with excess nitrate is not advisable because it results in human ailment. In this study, spinach leaves were harvested from plants grown under nine varying (0 to 400 kg/ha) nitrogenous fertilizer doses. A total of 261 samples were used to predict the nitrate content in spinach leaves using Vis-NIR (350 to 2,500 nm). The nitrate content was measured destructively using the ion-selective conductive method. Partial least square (PLS) regression models were developed using whole spectra and featured wavelengths. Spectral data were pre-processed using different spectral pre-processing techniques such as Savitzky-Golay (SG) derivative, standard normal variate (SNV), multiplicative scatter correction (MSC), baseline correction, and detrending. The predictive accuracy of the PLS model had improved after pre-processing of spectral data with MSC (RPDCV = 1.767; SECV = 545.745; biasCV = -3.107; slopeCV = 0.698) and SNV (RPDCV = 1.768; SECV = 545.337; biasCV = -3.201; slopeCV = 0.698) technique, but this was not significant (P < 0.05) as compared with raw spectral data (RPDCV = 1.679; SECV = 572.669; biasCV = -7.046; slopeCV = 0.687). The effective wavelengths for measurement nitrate content in spinach leaves were identified as 558, 706, 780, 1,000, and 1,420 nm. The performance of PLS model developed with effective wavelengths also had good prediction accuracy (RPDCV = 1.482; SECV = 648.672; biasCV = -3.805; slopeCV = 0.565) but significantly lower than the performance of model developed with full spectral data. The overall results of this study suggest that Vis-NIR spectroscopy can be an important tool and has great potential for the rapid and nondestructive assessment of nitrate content in harvested spinach, with a view to ascertain the suitability of the harvest for food uses. PRACTICAL APPLICATION: Better production and brighter color of leafy vegetable drive the farming community to overuse nitrogenous fertilizer. This has resulted in higher nitrate content in vegetables. It has been widely reported that consumption of these vegetables has carcinogenic effects on human beings. The prediction of nitrate content in leafy vegetables by traditional methods is time-consuming (30 min, including sample preparation time), destructive, and tedious; moreover, it cannot be used for inline applications. This study reports spectroscopy-based rapid (<5 s) assessment technique for nitrate measurement. A multivariable PLS model was developed using wavelengths representing nitrate content. This model can be adopted by food industries for inline applications.