A chemometric approach to assess the oil composition and content of microwave-treated mustard (Brassica juncea) seeds using Vis-NIR-SWIR hyperspectral imaging.

The wide gap between the demand and supply of edible mustard oil can be overcome to a certain extent by enhancing the oil-recovery during mechanical oil expression. It has been reported that microwave (MW) pre-treatment of mustard seeds can have a positive effect on the availability of mechanically expressible oil. Hyperspectral imaging (HSI) was used to understand the change in spatial spread of oil in the microwave (MW) treated seeds with bed thickness and time of exposure as variables, using visible near-infrared (Vis-NIR, 400-1000 nm) and short-wave infrared (SWIR, 1000-1700 nm) systems. The spectral data was analysed using chemometric techniques such as partial least square discriminant analysis (PLS-DA) and regression (PLSR) to develop prediction models. The PLS-DA model demonstrated a strong capability to classify the mustard seeds subjected to different MW pre-treatments from control samples with a high accuracy level of 96.6 and 99.5% for Vis-NIR and SWIR-HSI, respectively. PLSR model developed with SWIR-HSI spectral data predicted (R2 > 0.90) the oil content and fatty acid components such as oleic acid, erucic acid, saturated fatty acids, and PUFAs closest to the results obtained by analytical techniques. However, these predictions (R2 > 0.70) were less accurate while using the Vis-NIR spectral data.

Understanding the variations in dielectric properties of mustard (Brassica nigra L.) and argemone (Argemone mexicana) oil blends at different temperatures.

Mustard oil is the most commonly adulterated edible oil, invariably with argemone oil. This study was aimed to develop a parallel plate capacitive sensor for measurement of dielectric properties of pure mustard oil, pure argemone oil and their blends (25, 50 and 75%) at five levels of varying temperature (10 to 50 °C). The effect of blend ratio and temperature on the selected dielectric properties of oil-capacitance (C), dielectric loss tangent (tanδ), dielectric constant ( ε ' ), dielectric loss factor ( ε ″ ) and electrical conductivity (σ) were investigated. It was observed that composition of the individual oils in terms of moisture and fatty acids influenced the physical and dielectric properties. The sensor was used to relate the dielectric properties of oil samples with blend ratio and temperature by means of statistically significant (p < 0.05) and robust (R 2 > 0.8) multiple linear regression model. The effect of temperature on C and ε ' was negative, while it was otherwise for tanδ, ε ″ and σ. Increase in argemone oil content in the blends, increased the dielectric measures due to the associated changes in the physical and chemical properties. The capacitive sensor could distinctly identify mustard oil, argemone oil and its blends on the basis of dielectric properties.

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.

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.

Process optimization for a nutritious low-calorie high-fiber whey-based ready-to-serve watermelon beverage.

Whey is a nutritious by product of some traditional Indian processed milk products and it needs to be utilized in an effective way in order to reduce environmental hazards associated with its untreated disposal. Low calorie watermelon beverage appears to be a simple, attractive and economic method of whey disposal. The experiment was designed by Central Composite Rotatable Design of Responce Surface Methodology. Three independent variables whey, Innova ® fiber and sucralose were chosen at five levels within the respective ranges of 40-60 %, 2.0-5.0 % and 0.01-0.03 %. The effect of the variables on flavour, mouthfeel, after-taste, viscosity, total soluble solids (all to be maximized) and sedimentation (to be minimized) was observed. These three were the independent variables whose effect on flavour, mouthfeel, after-taste, viscosity, total soluble solids (all to be maximized) and sedimentation (to be minimized) were evaluated. Quadratic model fitted well to all dependent variables. The R(2) values for flavour, mouthfeel, aftertaste, viscosity, sedimentation and TSS were 95.57, 98.71, 95.50, 97.87, 99.26 and 98.17 %, respectively. Response surface methodology was used to optimize the level of processing parameters. Maximum scores for flavour (7.46), mouthfeel (7.49), after-taste (7.72), viscosity (13.55 cp) and total soluble solid (15.34°Brix) and minimum score for sedimentation (1.55 ml/10 ml) were obtained when the formulation contained 51.46 % whey, 3.84 % Innova® fiber and 0.021 % sucralose.

Process optimization for enzyme aided clarification of watermelon juice.

Watermelon juice was exposed to the enzyme masazyme at varying enzyme concentrations (0.01-0.1 % w/w) and different time (20-120 min) and temperature (30-50 °C) combinations. The effects of the treatments on selected responses (juice recovery, total dissolved solids (TDS), viscosity, turbidity, cloud stability and L value) were determined employing a second order Box Behnken Design in combination with Response Surface Methodology. Enzymatic treatment effectively degraded polysaccharides, resulting in reduced viscosity, turbidity and absorbance value and increased juice recovery, total dissolved solids and lightness. R(2) value for all models for the dependent variables were greater than 90 %. The maximum juice recovery (86.27 %), TDS (8.7°Brix) and L value (17.57) while minimum viscosity (0.0020 Pa.s.), turbidity (39.37 NTU) and cloud stability (0.033 abs) were obtained when enzyme treatment was set up with 0.09 % w/w enzyme concentration at 46.90 °C and 117.45 min.

Influence of processing parameters on textural characteristics and overall acceptability of millet enriched biscuits using response surface methodology.

Five blends of refined wheat flour (RWF) (63.2 - 96.8, %RWF) and millet were used to manufacture biscuits baked for varying time (3.3-6.7 min) and temperature (166.6 - 183.4 °C). The manufactured biscuits were evaluated in terms of textural attributes (crispness, hardness and cutting strength) and overall acceptability (OAA). Results showed that increasing the amount of RWF in biscuits decreased (p < 0.01) hardness. Prolonging the baking time led to a decrease (p < 0.01) in hardness and cutting strength and a significant increase (p < 0.05) in OAA. Increase in baking temperature was followed by an increase in crispness (p < 0.01) and OAA (p < 0.1), while hardness and cutting strength (p < 0.01) decreased. Optimum processing condition generated form the models was, - blend ratio, 90%RWF; baking time, 6 min and baking temperature, 170 °C. The predicted responses in terms of crispness, hardness, cutting strength and OAA were 45, 0.3N, 27.2N and 8.9, respectively. The desirability of the optimum conditions was 0.98.