Soft Computing in Food Processing


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Dr. R. T. Patil Former Director CIPHET, Pr. Scientist, Central Institute of Agricultural Engineering, Bhopal Application of Soft Computing in Food Processing Sector

Status of Food Processing Industries: 

Status of Food Processing Industries Size of food market in India - Rs. 8,60,000 Crores Primarily processed food market – Rs. 2,80,000 crore Value added processed food market – Rs. 1,80,000 crore Investment during the 10th plan is estimated at Rs. 62,105 Crores Industry growth rate during the last five years is estimated at 7.14% against GDP of 6.2% Investment required during next ten years – Rs. 1,10,000 crores Food Quality and Safety Issues are Prime Important

Hard Computing Vs Soft Computing in the Context of Food Processing: 

Hard Computing Vs Soft Computing in the Context of Food Processing Hard computing requires a precisely stated analytical model and often a lot of computation time where as the role model for soft computing is the human mind. I n soft computing the tolerance for imprecision and uncertainty is exploited to achieve tractability, lower cost, high Machine Intelligence Quotient (MIQ) and economy of communication Hard computing uses two-valued logic; soft computing can use multivalued or fuzzy logic Hard computing requires exact input data; soft computing can deal with ambiguous and noisy data Hard computing produces precise answers; soft computing can yield approximate answers

Soft Computing : 

Soft Computing Soft computing can be used to model and analyse very complex problems where conventional methods have not been able to produce cost-effective, analytical, or complete solutions. In agricultural and biological engineering, researchers and engineers have developed methods to analyse the operation of food processing. Fuzzy Logic (FL), Artificial Neural Networks (ANNs), Genetic Algorithms (Gas), Bayesian Inference (BI), Decision Tree (DT), and Support Vector Machines (SVMs)

Food Processing Control: 

Food Processing Control There are many parameters in food industry that must be taken into consideration in parallel. A single sensory property like color or texture can be linked individually to several dimensions recorded by the human brain. The food industry works with non-uniform, variable raw materials that, when processed, should shaped into a product that satisfies a fixed standard. The process control of foods are highly non-linear and variables are coupled. In addition to the temperature changes during a heating or cooling process, there are biochemical (nutrient, color , flavor , etc.) or microbial changes that should be considered. The moisture in food is constantly fluctuating either loss or gain throughout the process which can affect the flavor , texture, nutrients concentration and other properties. Other properties of foods such as density, thermal and electrical conductivity, specific heat, viscosity, permeability, and effective moisture diffusivity are often a function of composition, temperature, and moisture content, and therefore keep changing during the process. The system is also quite non-homogeneous and hence detailed input data are not available. Often, irregular shapes are present.

Simple Fuzzy Logic Control for Food Processing: 

Simple Fuzzy Logic Control for Food Processing 1. Start with a proportional–integral–derivative (PID) controller. 2. Insert an equivalent, linear fuzzy controller. 3. Make it gradually nonlinear.

ANN for Crispness of Snack Foods: 

ANN for Crispness of Snack Foods The crispness was evaluated by acoustic testing. The acoustic patterns were generated by crushing the snack samples with a pair of pincers The inputs for training the NNs comprised 102 amplitudes of sound signals in 0–7 kHz frequency range at the intervals of about 69 Hz with crispness grades as outputs P robabilistic (PNN) models showed good performance in classifying the snack foods into four grades of crispness. The prediction accuracy of models ranged approximately from 96 to 98% Plot of average amplitude of acoustical signal spectrum for different moisture content of Pringles potato chip samples

Meat Quality Using Hyperspectral Imaging and Support Vector Machines.: 

Meat Quality Using Hyperspectral Imaging and Support Vector Machines . In order to predict the total viable count (TVC) of bacteria of pork meat, least square support vector machines (LS-SVM) was adopted as the modeling method. The prediction model based on the optimal five wavelengths was able to predict TVC with r = 0.87 and the result was considerably better than that of ANNs and MLR method. hyperspectral imaging system coupled with the modeling method based on LS-SVM is a valid means for nondestructive determination of TVC of pork meat.

Parameter Estimation of Twin-Screw Food Extrusion Process using Genetic Algorithms: 

Parameter Estimation of Twin-Screw Food Extrusion Process using Genetic Algorithms The common approach is to determine the operating conditions and then to maintain these values as closely as possible using various control loops, if not manual control. GAs work with a coding of the parameter set, not the parameters themselves. Secondly, the algorithms search from the population of points, climbing many picks in parallel GA only require object function values to guide their search, but they have no need for derivative or other auxiliary information. Algorithms use probabilistic rather than deterministic transition rules to guide their search. Hence genetic algorithm is robust and offers advantage over other more commonly used optimisation techniques

Bayesian Inference to Classify White Grape Varieties: 

Bayesian Inference to Classify White Grape Varieties The fusion method based on the Bayesian inference was used to to combine the outputs of various sensors for white grapes varieties. The sensors were aroma sensors, FT-IR and UV spectrometers. Two methods were developed based on the Bayesian inference: the Bayesian minimum error fusion rule and the minimum risk rule. The effective fusion method lead to a significant improvement in the grape variety discrimination: the final misclassification error was 4.7%, whereas the best individual sensor (FT-IR) gave a misclassification error as 9.6%. Bayesian fusion proved to be very well suited to the combination of all kinds of analytical measurements with ability to cope with sensors providing large, noisy and redundant data as well as sensors having dissimilar efficiency levels.

Decision Tree to Identify Food Additives and Processing Aids: 

Decision T ree to I dentify F ood A dditives and P rocessing A ids Question 1: Does the definition of food additive exclude the substance from being a food additive? Question 2: Does use of the substance affect one or more characteristics of the food? Question 3: Does the substance become part of the food? Question 4: Are residues of the substance in the food "negligible" in accordance with this policy?

Detection of Plant Diseases: 

Detection of Plant Diseases Electronic nose incorporating artificial intelligence was used to detect plant disease, specifically basal stem rot (BSR) disease that is caused by Ganoderma boninense fungus affecting oil palm plantations in South East Asia. The commercially available electronic nose, Cyranose 320, as the front end sensors and artificial neural networks for pattern recognition. The odour samples were captured on site and the classification performed on a PC. The system was able to differentiate healthy and infected oil palm with a high rate of accuracy.

Automatic Fruit and Vegetable Classification from Images: 

Automatic Fruit and Vegetable Classification from Images Face recognition, fingerprinting identification, image categorization, and DNA sequencing is high tech application The fusion approach was validated using a multi-class fruit-and-vegetable categorization task in a semi-controlled environment, such as a distribution center The results show that the solution is able to reduce the classification error in up to 15 percentage points with respect to the baseline.

ANN in image recognition and classification of crop and weeds: 

ANN in image recognition and classification of crop and weeds The images were taken . Colour index values were assigned to the pixels of the indexed image and used as ANN inputs. There were 80 images, 100x100 pixels, for training, and 20 images for testing. Many back propagation ANN models were developed with different numbers of PEs in their hidden and various output layers. Six different evaluation schemes for two ANN output strategies were used. The performance of the ANNs was compared and the success rate for the identification of corn was observed to be as high as 80 to 100%, while the success rate for weed classification was as high as 60 to 80%. The results indicated the potential of ANNs for fast image recognition and classification. Fast image recognition and classification can be useful in the control of real-world, site-specific herbicide application.

Soft Computing in Food Processing Applications: 

Soft Computing in Food Processing Applications 2004 Brudzewski et al. Classification of milk by an electronic nose 2005 Pierna et al. Classification of modified starches 2006 Chen et al. Identification of tea varieties 2006 Onaran et al. Detection of underdeveloped hazenuts from fully developed nuts 2006 Wang and Paliwal Discrimination of wheat classes 2007 Zhang et al. Differentiate individual fungal infected and healthy wheat kernels. 2008 Fu et al. Quantification of vitamin C content in kiwifruit 2008 Kovacs et al. Prediction of different concentration classes of instant coffee with electronic tongue 2008 Li et al. Classification of paddy seeds by harvest year 2008 Sun et al. On-line assessing internal quality of pears 2008 Wu et al. Identification of varieties of Chinese cabbage seeds 2009 Deng et al. Classification of intact and cracked eggs 2012 Jha et al. Method of determining maturity of intact mango in tree

Researchable Issues: 

Researchable Issues Online non destructive measurement of quality of food grains, fruits and vegetables using NIR sensors Electronic nose to assess the quality and authenticity of food products. Electronic tongue - for recognition (identification, classification, discrimination), quantitative multi-component analysis and artificial assessment of taste and flavour of various liquids Affordable instrumentation for measurement of spoilage of grain in bags and silos Smart labels of food packets to detect their shelf life with automatically changing bar codes Simple gadgets like pH meter to detect pollutants in drinking water

PowerPoint Presentation: 

Conclusions No matter which soft computing method is used, adaptive learning is essential to exploit the potential synergy between methods. Another trend in soft computing applications is likely to be the fusion of soft computing and hard computing. Hard and soft computing fusion in agricultural and biological engineering has just begun and hence shows great potential for future research in this sector.

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