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.

Rheological, nutritional, functional and sensory properties of millets and sprouted legume based beverages.

The present study investigated the effect of processing on the nutritional, functional, organoleptic and rheological properties of millet and sprouted legumes beverage flavored with jaggery (B1) and buttermilk (B2) for its processing suitability. The millets, sprouted legumes, flavoured with jaggery (B1); and buttermilk and salt (B2), used influenced the suspension stability, nutritional, sensory quality and rheology of beverages. Millets imparted minerals and starch while sprouted legumes improved solubility and extractability of nutrients and also increased the levels of anti-oxidants and flavonoids. Buttermilk improved the stability, increased contents of proteins and minerals and imparted a light colour to the beverage. Jaggery was responsible for caramelized colour and flavor, improved consistency, psuedoplasticity and better organoleptic acceptability. The nutritional quality of B2 was higher in terms of iron (1.8 mg/100 g) and calcium (75 mg/100 g) with 90% antioxidant activity. Highest L* values were obtained for B2 indicating lighter color, whereas B1 was darker with lower L* values. Organoleptic evaluation showed higher acceptability (7.6) of B1 as compared to B2. Results of flow behavior indicated pseudo-plastic nature of beverages. Significant increase in viscosity was also observed with the increase in temperature (10, 25 and 45 °C) of the beverages. The flow curves of B1 produced the best fit applying the Power law model and for B2, Casson model was the best fit. Results of this study could be used in improvement of the process for making millet-based beverage, design of packaging system and also to predict the flow behavior of beverages during storage.

Effect of composition and storage time on some physico-chemical and rheological properties of probiotic soy-cheese spread.

Probiotic soy-cheese spread was prepared by fermenting soymilk with specific probiotic starter culture, and there after processing the coagulated mass. Soy cheese spread samples had more than 109 cfu/g of viable probiotic count at the time of preparation; and had around 17.6% protein, 25.3% fat and 19.8% total soluble sugar. Compared to commercially available dairy cheese spread, probiotic soy cheese spread had significantly higher protein and anti-oxidant activity. Soy cheese spreads, prepared from pure soymilk as well as by mixing with dairy milk, were studied with respect to the differences in their rheological behavior during storage at refrigerated conditions. A dynamic oscillatory test was used to measure the viscoelastic properties of spreads at 0, 7, 14, 21 and 28 days of storage. It was observed that the storage modulus (G') was higher than the loss modulus (G″) throughout the storage period indicating that the soy cheese spreads exhibit predominantly elastic behavior. The cheese spread sample prepared by adding okara in soymilk had the highest values of G' and complex viscosity (1120 Pa and 11.5 Pa s, respectively at an angular frequency of 100 s-1). G', G″ and viscosity of cheese spread did not change significantly up to 14 days, with values of 650, 225 Pa and 7.43 Pa s, respectively for the sample prepared from soymilk alone. However, these values increased thereafter which might be an indication of structural changes in the cheese spread samples.

Quality characteristics of sauerkraut fermented by using a Lactobacillus paracasei starter culture grown in tofu whey.

The quality parameters of sauerkraut fermented using Lactobacillus paracasei in terms of its lactic acid bacteria count, texture, colour and biochemical properties were studied. As a starter culture L. paracasei grown in tofu whey was used for sauerkraut fermentation. The experiments were planned using central composite rotatable design of response surface methodology for input variables - culture volume (ml), fermentation time (days) and salt concentration (g/100 g). The linear and interactive effect of variables on responses was understood by statistically significant (p < 0.01) second-order models. Amongst all the input variables culture volume was found to have an overwhelming effect over all the responses. There was a significant (p < 0.01) increase in the lactic acid bacteria count of finished product; it was less hard but there was a departure in colour from the traditional product. The optimized condition for sauerkraut fermentation in terms of culture volume (ml), fermentation time (day) and salt concentration (g/100 g) was 30 ml, 28 days and 1 g/100 g, respectively. It was also observed that phenolics content was better in starter culture sauerkraut over the one traditionally prepared.

Quality characteristics of gluten free bread from barnyard millet-soy flour blends.

The effects of formulation of leavened bread by using varying levels (for 50 g base flour) of soy flour-barnyard millet blends (with 5.74, 6.25, 7, 7.75 and 8.26 g of soy flour), yeast (1.83, 2, 2.25, 2.5 and 2.67 g) and salt (0.63, 0.8, 1.05, 1.30 and 1.47 g) on textural, colour and specific volume were determined. A central composite rotatable design of response surface methodology was used to plan the experiments. The second order models obtained were observed to be statistically significant and capable of demonstrating the effects input variables on responses. All the textural properties were affected significantly by amount of soy flour and yeast in the dough. Soy flour had a significant effect on the colour of the bread making it more brown. Interaction of soy flour and yeast affected the specific volume to maximum extent. Two-tailed t test established that the efficacy of the models as no significant was observed between the predicted and the actual values.

Evaluation of nutritional, textural and particle size characteristics of dough and biscuits made from composite flours containing sprouted and malted ingredients.

Composite flours (CF) using cereals, legumes, millets, soy-protein isolate, dairy ingredient and fruit without refined flour were used for preparing multi-nutrient biscuits. Dough and biscuits were evaluated for physical, nutritional and textural properties, particle size, colour and sensory evaluation and compared against refined-flour biscuits (C). Effect of malting and sprouting on biscuit quality were also analyzed. The highest volume of particles for CF was 140 µm higher than C flour. CF biscuits had significantly (p ≤ 0.05) lower spread ratio and % weight loss compared to C. The hardness, stickiness and cohesiveness values of CF doughs were significantly (p ≤ 0.05) lower than C resulting in lower cutting strength and increased hardness of CF biscuits. Sprouting further decreased hardness of CF dough. Nutrient content of CF biscuits (sprouted and unsprouted) were significantly (p ≤ 0.05) higher than C biscuits. Sensory evaluation showed CF biscuits especially with sprouted flour had higher acceptability and were superior to C.

X-ray imaging methods for internal quality evaluation of agricultural produce.

A number of non-destructive methods for internal quality evaluation have been studied by different researchers over the past eight decades. X-ray and computed tomography imaging techniques are few of them which are gaining popularity now days in various fields of agriculture and food quality evaluation. These techniques, so far predominantly used in medical applications, have also been explored for internal quality inspection of various agricultural products non-destructively, when quality features are not visible on the surface of the products. Though, safety of operators and time required for tests are of concern, the non-destructive nature of these techniques has great potential for wide applications on agricultural produce. This paper presents insight of X-ray based non-destructive techniques such as X-ray imaging and Computed Tomography (CT). The concepts, properties, equipment and their parameters, systems and applications associated with the use of X-rays and CT for agricultural produce have been elaborated.