MICROWAVE DIELECTRIC SPECTRA AND THE COMPOSITION OF FOODS: PRINCIPAL COMPONENT ANALYSIS VERSUS ARTIFICIAL NEURAL NETWORKS.

Similar documents
Shelf life prediction of paneer tikka by artificial neural networks

Metallized Polyester Film Capacitors (MKT-S) Axial Leads B High reliability capacitor Encapsulated in metal tube

Wine-Tasting by Numbers: Using Binary Logistic Regression to Reveal the Preferences of Experts

Hybrid ARIMA-ANN Modelling for Forecasting the Price of Robusta Coffee in India

Lead spacing Diameter d 1 Type

Lead spacing Diameter d 1 Type

Metallized Polyester Film Capacitors (MKT) in Plastic Case B B Standard applications

Computerized Models for Shelf Life Prediction of Post-Harvest Coffee Sterilized Milk Drink

XVII th World Congress of the International Commission of Agricultural and biosystems Engineering (CIGR)

Multiple Imputation for Missing Data in KLoSA

Grain and Flour Quality of Ethiopian Sorghum in Respect of their Injera Making Potential

What makes a good muffin? Ivan Ivanov. CS229 Final Project

From VOC to IPA: This Beer s For You!

Decision making with incomplete information Some new developments. Rudolf Vetschera University of Vienna. Tamkang University May 15, 2017

Liquidity and Risk Premia in Electricity Futures Markets

Coffea arabica var. laurina Authentication Using Near Infrared Spectroscopy

Mobility tools and use: Accessibility s role in Switzerland

Wine Rating Prediction

Missing Data Methods (Part I): Multiple Imputation. Advanced Multivariate Statistical Methods Workshop

Relation between Grape Wine Quality and Related Physicochemical Indexes

Missing value imputation in SAS: an intro to Proc MI and MIANALYZE

Modeling Wine Quality Using Classification and Regression. Mario Wijaya MGT 8803 November 28, 2017

Predicting Wine Quality

Washed agar gave such satisfactory results in the milk-powder. briefly the results of this work and to show the effect of washing

EFFECTS OF CONVENTIONAL AND MICROWAVE COOKING METHODS ON CHARACTERISTICS OF REFORMED BEEF ROASTS

Effective Classification of Chinese Tea Samples in Hyperspectral Imaging

PREDICTION OF WINE SENSORIAL QUALITY BY ROUTINELY MEASURED CHEMICAL PROPERTIES

RELATIVE EFFICIENCY OF ESTIMATES BASED ON PERCENTAGES OF MISSINGNESS USING THREE IMPUTATION NUMBERS IN MULTIPLE IMPUTATION ANALYSIS ABSTRACT

AppNote 4/2003. Fast Analysis of Beverages using a Mass Spectral Based Chemical Sensor KEYWORDS ABSTRACT

Corn and Soybean CORN OUTLOOK SOYBEAN OUTLOOK STATISTICS AND ANALYSIS

Analysis of Things (AoT)

Quality of Canadian non-food grade soybeans 2014

A Hedonic Analysis of Retail Italian Vinegars. Summary. The Model. Vinegar. Methodology. Survey. Results. Concluding remarks.

Application & Method. doughlab. Torque. 10 min. Time. Dough Rheometer with Variable Temperature & Mixing Energy. Standard Method: AACCI

MUSSELING UP MATT MILLER NZ FATS AND OILS NOV 2016

Method for the imputation of the earnings variable in the Belgian LFS

Dietary Diversity in Urban and Rural China: An Endogenous Variety Approach

OC Curves in QC Applied to Sampling for Mycotoxins in Coffee

AppNote 2/2003. Wine Discrimination using a Mass Spectral Based Chemical Sensor KEYWORDS ABSTRACT

for forecasting unit price of tea at Colombo auction

Evaluation of univariate time series models for forecasting of coffee export in India

The multivariate piecewise linear growth model for ZHeight and zbmi can be expressed as:

1.3 Box & Whisker Plots

March The newborn calf 3/14/2016. Risks and Benefits of Milk vs. Milk Replacers for. Low milk prices???? Incentive to lower SCC?

STEP1 Check the ingredients used for cooking, their weight, and cooking method. Table19 Ingredient name and weight of company A s Chop Suey

ESTIMATING BIOCHEMICAL PARAMETERS OF TEA (CAMELLIA SINENSIS (L.)) USING HYPERSPECTRAL TECHNIQUES

Imputation of multivariate continuous data with non-ignorable missingness

Development of smoke taint risk management tools for vignerons and land managers

Using Growing Degree Hours Accumulated Thirty Days after Bloom to Help Growers Predict Difficult Fruit Sizing Years

Corn Quality for Alkaline Cooking: Analytical Challenges

The Market Potential for Exporting Bottled Wine to Mainland China (PRC)

Quality of Canadian oilseed-type soybeans 2016

Tree Rings and Water Resource Management in the Southwest

Growth in early yyears: statistical and clinical insights

( ) Plastic Film Capacitors. Polyester Film Capacitor E C Q B E C Q B

Determination of Caffeine in Coffee Products According to DIN 20481

Handling Missing Data. Ashley Parker EDU 7312

PEEL RIVER HEALTH ASSESSMENT

Quality of Canadian oilseed-type soybeans 2017

The Importance of Dose Rate and Contact Time in the Use of Oak Alternatives

F&N 453 Project Written Report. TITLE: Effect of wheat germ substituted for 10%, 20%, and 30% of all purpose flour by

Definition of Honey and Honey Products

THE GROWTH OF THE CHERRY OF ROBUSTA COFFEE

Electronic Nose Based on Independent Component Analysis Combined with Partial Least Squares and Artificial Neural Networks for Wine Prediction

Biosignal Processing Mari Karsikas

antipasto 57 cream 54 eggs 55 pastry butter 56 cream cheese 53 margarine 56 smallgoods cheese 48 dips noodles yogurt 53 fridge fridge

Increasing Toast Character in French Oak Profiles

ICAR General Assembly and Management Workshop Poreč (Croatia) May 2009

THE STATISTICAL SOMMELIER

USDA. Product & Ingredient Naming

*p <.05. **p <.01. ***p <.001.

AJAE Appendix: Testing Household-Specific Explanations for the Inverse Productivity Relationship

Two kinds of tests were carried out: mechanical and chemical tests.

Fair Trade and Free Entry: Can a Disequilibrium Market Serve as a Development Tool? Online Appendix September 2014

Parameters Effecting on Head Brown Rice Recovery and Energy Consumption of Rubber Roll and Stone Disk Dehusking

WINE RECOGNITION ANALYSIS BY USING DATA MINING

COMPARISON OF THREE METHODOLOGIES TO IDENTIFY DRIVERS OF LIKING OF MILK DESSERTS

OF THE VARIOUS DECIDUOUS and

1. Continuing the development and validation of mobile sensors. 3. Identifying and establishing variable rate management field trials

Abstract. Introduction

DEVELOPMENT AND STANDARDISATION OF FORMULATED BAKED PRODUCTS USING MILLETS

The R survey package used in these examples is version 3.22 and was run under R v2.7 on a PC.

Quality of western Canadian flaxseed 2012

Identification of Adulteration or origins of whisky and alcohol with the Electronic Nose

RESOLUTION OIV-OENO MONOGRAPH ON GLUTATHIONE

J. M. C. Dang 1 and M. L. Bason 1,2

wine 1 wine 2 wine 3 person person person person person

Health Effects due to the Reduction of Benzene Emission in Japan

THE EFFECTS OF FINAL MOLASSES AND SUGAR PURITY VALUES ON THE CALCULATION OF 96 0 SUGAR AND FACTORY RECOVERY INDEX. Heera Singh

Labor Supply of Married Couples in the Formal and Informal Sectors in Thailand

CLASSIFICATION OF ARABICA AND ROBUSTA COFFEE USING ELECTRONIC NOSE

M. Swanepoel 1,2, M. du Toit 1 and H.H. Nieuwoudt 1*

Smoke Taint Risk Management Tools

Evaluation of the Nietvoorbi j Wine Score Card and Experimental Wine Panelists Utilizing Pattern Recognition Techniques

The Development of a Weather-based Crop Disaster Program

Pitfalls for the Construction of a Welfare Indicator: An Experimental Analysis of the Better Life Index

DIR2017. Training Neural Rankers with Weak Supervision. Mostafa Dehghani, Hamed Zamani, Aliaksei Severyn, Sascha Rothe, Jaap Kamps, and W.

Forestry, Leduc, AB, T9E 7C5, Canada. Agriculture/Forestry Centre, Edmonton, AB T6G 2P5, Canada. *

CONTRIBUTIONS FROM ECONOMICS TOWARDS THE IMPROVEMENT

Effect of packing type and storage time on tea (Camellia sinensis L.) seed germination

Transcription:

MICROWAVE DIELECTRIC SPECTRA AND THE COMPOSITION OF FOODS: PRINCIPAL COMPONENT ANALYSIS VERSUS ARTIFICIAL NEURAL NETWORKS.

Michael Kent, Frank Daschner, Reinhard Knöchel Christian Albrechts University of Kiel, Germany.

Complex dielectric permittivity e* f = e f - je f Dispersion, energy storage Loss factor, energy dissipation

Dielectric spectra for Chicken at 30 C: 2 hours in 2% polyphosphate 100 90 80 70 60 e',e" 50 40 30 20 10 outlier mean 10 x s.d. replicates 0 1.0E+08 1.0E+09 1.0E+10 1.0E+11 frequency (Hz)

Multivariate analysis

Principal Component Analysis (PCA) Y j = c 1j X 1 + c 2j X 2 +.. c pj X p.

45.00 PC1 40.00 PC2 e' at 12GHz 35.00 30.00 25.00 20.00 35.00 37.00 39.00 41.00 43.00 45.00 47.00 49.00 51.00 e' at 6GHz

Principal Component Analysis (PCA) Y j = c 1j X 1 + c 2j X 2 +.. c pj X p X values are collinear,

Principal Component Analysis (PCA) Y j = c 1j X 1 + c 2j X 2 +.. c pj X p X values are collinear, Y values are orthogonal.

Principal Component Analysis (PCA) Y j = c 1j X 1 + c 2j X 2 +.. c pj X p X values are collinear, Y values are orthogonal. Data reduction: n values of Y replace p>n values of X

Principal Component Analysis (PCA) Y j = c 1j X 1 + c 2j X 2 +.. c pj X p X values are collinear, Y values are orthogonal. Data reduction: n values of Y replace p>n values of X Principal component Regression (PCR)

Principal Component Analysis (PCA) Y j = c 1j X 1 + c 2j X 2 +.. c pj X p X values are collinear, Y values are orthogonal. Data reduction: n values of Y replace p>n values of X Principal component Regression (PCR) k responses y, n Principal Components Y y k = a k + 1 n b ki Y i

Partial Least Squares Regression (PLSR)

Partial Least Squares Regression (PLSR) Similar to PCR but y values included in optimisation process.

Partial Least Squares Regression (PLSR) Similar to PCR but y values included in optimisation process. Constructs linear combinations of inputs incorporating y

Partial Least Squares Regression (PLSR) Similar to PCR but y values included in optimisation process. Construct linear combinations of inputs incorporating y Finds directions with maximum variance and correlation with the output

Partial Least Squares Regression (PLSR) Similar to PCR but y values included in optimisation process. Construct linear combinations of inputs incorporating y Finds directions with maximum variance and correlation with the output Partial least squares regression operates like principal component regression

Principal Components Regression Regress on n < p principal components of X

Principal Components Regression Regress on n < p principal components of X Partial Least Squares Regression Regress on n < p directions of X weighted by y

Artificial Neural Networks (ANN)

Artificial Neural Networks (ANN) y = f(x,w)

Artificial Neural Networks (ANN) X variables measured y = f(x,w)

Artificial Neural Networks (ANN) y = f(x,w) X variables measured W weighting vector or activation function

Artificial Neural Networks (ANN) Activation function of neurons

Artificial Neural Networks (ANN) Activation function of neurons: linear-emulates PCR

Artificial Neural Networks (ANN) Activation function of neurons: linear-emulates PCR non-linear- more general

Artificial Neural Networks (ANN) Activation function of neurons: linear-emulates PCR non-linear- more general- sigmoid, tansig or logsig tansig(n)=2/(1+exp(-2n))-2

Samples and data used

chicken 1 calibration 144 samples measured in 1994; 72 controls, 72 treated. validation 72 samples measured in 2000. chicken 2 calibration pooled data -106 validation samples. validation 110 randomly selected samples. prawns calibration 152 samples validation 16 samples prepared at a later date pork calibration 167 various types of treated pork, canned, bacon, ham and fresh validation 18 unknown samples prepared at a later date; 4 leg meat, 4 streaky bacon, 4 ham and 6

Some results

R 2 PCR RMSE C RMSE Chicken 1 Water 57.0 1.15 0.57 Protein 64.1 1.23 0.79 Added Water 89.5 3.91 2.30 Chicken 2 Water 69.3 0.75 0.70 Protein 69.0 0.96 0.99 Added Water 90.8 2.69 3.28 Prawns Water 82.6 0.65 0.82 Fat 36.8 0.09 0.08 Protein 89.1 0.58 0.92 NaCl 70.5 0.42 0.083 Phosphate 58.3 98.4 63.8 Added Water 74.6 3.06 3.81 Pork Water 94.2 1.53 1.43 Fat 95.3 1.59 1.63 Ash 88.6 0.24 0.20 Protein 66.3 1.78 1.38 NaCl 91.8 0.19 0.15 Added Water 54.4 7.60 5.90 V

R 2 PCR PLSR RMSE RMSE R 2 RMSE C V C RMSE Chicken 1 Water 57.0 1.15 0.57 69.5 0.97 0.70 Protein 64.1 1.23 0.79 72.7 1.09 1.04 Added Water 89.5 3.91 2.30 90.8 3.66 2.86 Chicken 2 Water 69.3 0.75 0.70 69.8 0.75 0.74 Protein 69.0 0.96 0.99 67.9 0.97 0.99 Added Water 90.8 2.69 3.28 91.9 2.53 3.30 Prawns Water 82.6 0.65 0.82 88.1 0.54 0.81 Fat 36.8 0.09 0.08 44.2 0.08 0.09 Protein 89.1 0.58 0.92 90.0 0.55 0.96 NaCl 70.5 0.42 0.083 83.4 0.031 0.092 Phosphate 58.3 98.4 63.8 58.9 97.6 63.8 Added Water 74.6 3.06 3.81 81.3 2.62 3.80 Pork Water 94.2 1.53 1.43 96.0 1.27 1.29 Fat 95.3 1.59 1.63 96.5 1.38 1.56 Ash 88.6 0.24 0.20 93.8 0.18 0.12 Protein 66.3 1.78 1.38 83.6 1.24 1.02 NaCl 91.8 0.19 0.15 94.0 0.17 0.087 Added Water 54.4 7.60 5.90 81.2 4.87 4.11 V

R 2 PCR PLSR ANN (linear) RMSE RMSE R 2 RMSE RMSE R 2 RMS C V C V E C Chicken 1 Water 57.0 1.15 0.57 69.5 0.97 0.70 80.9 0.77 0.86 Protein 64.1 1.23 0.79 72.7 1.09 1.04 74.5 1.05 1.15 Added Water 89.5 3.91 2.30 90.8 3.66 2.86 92.6 3.29 5.35 Chicken 2 Water 69.3 0.75 0.70 69.8 0.75 0.74 72.1 0.72 0.73 Protein 69.0 0.96 0.99 67.9 0.97 0.99 71.3 0.92 1.03 Added Water 90.8 2.69 3.28 91.9 2.53 3.30 91.2 2.64 3.25 Prawns Water 82.6 0.65 0.82 88.1 0.54 0.81 88.0 0.54 0.81 Fat 36.8 0.09 0.08 44.2 0.08 0.09 44.8 0.081 0.083 Protein 89.1 0.58 0.92 90.0 0.55 0.96 90.8 0.53 0.97 NaCl 70.5 0.42 0.083 83.4 0.031 0.092 75.5 0.038 0.088 Phosphate 58.3 98.4 63.8 58.9 97.6 63.8 60.9 95.2 67.3 Added Water 74.6 3.06 3.81 81.3 2.62 3.80 81.4 2.62 3.70 Pork Water 94.2 1.53 1.43 96.0 1.27 1.29 95.2 1.39 1.39 Fat 95.3 1.59 1.63 96.5 1.38 1.56 95.5 1.57 1.61 Ash 88.6 0.24 0.20 93.8 0.18 0.12 90.0 0.23 0.17 Protein 66.3 1.78 1.38 83.6 1.24 1.02 75.6 1.51 1.17 NaCl 91.8 0.19 0.15 94.0 0.17 0.087 92.5 0.19 0.12 Added Water 54.4 7.60 5.90 81.2 4.87 4.11 69.7 6.20 4.85 RMS E V

PCR PLSR ANN (linear) ANN(non-linear) R 2 RMSE C RMSE V R 2 RMSE C RMSE V R 2 RMS E C RMS E V R 2 RMSE C RMSE V Chicken 1 Water 57.0 1.15 0.57 69.5 0.97 0.70 80.9 0.77 0.86 87.2 0.63 0.64 Protein 64.1 1.23 0.79 72.7 1.09 1.04 74.5 1.05 1.15 74.9 1.04 0.77 Added Water 89.5 3.91 2.30 90.8 3.66 2.86 92.6 3.29 5.35 92.1 3.39 1.61 Chicken 2 Water 69.3 0.75 0.70 69.8 0.75 0.74 72.1 0.72 0.73 83.5 0.55 0.60 Protein 69.0 0.96 0.99 67.9 0.97 0.99 71.3 0.92 1.03 77.2 0.82 0.99 Added Water 90.8 2.69 3.28 91.9 2.53 3.30 91.2 2.64 3.25 96.6 1.63 2.42 Prawns Water 82.6 0.65 0.82 88.1 0.54 0.81 88.0 0.54 0.81 78.4 0.73 0.62 Fat 36.8 0.09 0.08 44.2 0.08 0.09 44.8 0.081 0.083 7.6 0.11 0.06 Protein 89.1 0.58 0.92 90.0 0.55 0.96 90.8 0.53 0.97 94.4 0.42 0.74 NaCl 70.5 0.42 0.083 83.4 0.031 0.092 75.5 0.038 0.088 55.7 0.51 0.64 Phosphate 58.3 98.4 63.8 58.9 97.6 63.8 60.9 95.2 67.3 83.9 61.1 58.9 Added Water 74.6 3.06 3.81 81.3 2.62 3.80 81.4 2.62 3.70 80.2 2.70 2.45 Pork Water 94.2 1.53 1.43 96.0 1.27 1.29 95.2 1.39 1.39 98.6 0.74 0.59 Fat 95.3 1.59 1.63 96.5 1.38 1.56 95.5 1.57 1.61 98.6 0.88 0.67 Ash 88.6 0.24 0.20 93.8 0.18 0.12 90.0 0.23 0.17 97.5 0.12 0.05 Protein 66.3 1.78 1.38 83.6 1.24 1.02 75.6 1.51 1.17 82.5 0.82 0.60 NaCl 91.8 0.19 0.15 94.0 0.17 0.087 92.5 0.19 0.12 95.1 0.15 0.042 Added Water 54.4 7.60 5.90 81.2 4.87 4.11 69.7 6.20 4.85 90.3 3.50 2.73

PCR PLSR ANN (linear) ANN(non-linear) R 2 RMSE C RMSE V R 2 RMSE C RMSE V R 2 RMS E C RMS E V R 2 RMSE C RMSE V Chicken 1 Water 57.0 1.15 0.57 69.5 0.97 0.70 80.9 0.77 0.86 87.2 0.63 0.64 Protein 64.1 1.23 0.79 72.7 1.09 1.04 74.5 1.05 1.15 74.9 1.04 0.77 Added Water 89.5 3.91 2.30 90.8 3.66 2.86 92.6 3.29 5.35 92.1 3.39 1.61 Chicken 2 Water 69.3 0.75 0.70 69.8 0.75 0.74 72.1 0.72 0.73 83.5 0.55 0.60 Protein 69.0 0.96 0.99 67.9 0.97 0.99 71.3 0.92 1.03 77.2 0.82 0.99 Added Water 90.8 2.69 3.28 91.9 2.53 3.30 91.2 2.64 3.25 96.6 1.63 2.42 Prawns Water 82.6 0.65 0.82 88.1 0.54 0.81 88.0 0.54 0.81 78.4 0.73 0.62 Fat 36.8 0.09 0.08 44.2 0.08 0.09 44.8 0.081 0.083 7.6 0.11 0.06 Protein 89.1 0.58 0.92 90.0 0.55 0.96 90.8 0.53 0.97 94.4 0.42 0.74 NaCl 70.5 0.42 0.083 83.4 0.031 0.092 75.5 0.038 0.088 55.7 0.51 0.64 Phosphate 58.3 98.4 63.8 58.9 97.6 63.8 60.9 95.2 67.3 83.9 61.1 58.9 Added Water 74.6 3.06 3.81 81.3 2.62 3.80 81.4 2.62 3.70 80.2 2.70 2.45 Pork Water 94.2 1.53 1.43 96.0 1.27 1.29 95.2 1.39 1.39 98.6 0.74 0.59 Fat 95.3 1.59 1.63 96.5 1.38 1.56 95.5 1.57 1.61 98.6 0.88 0.67 Ash 88.6 0.24 0.20 93.8 0.18 0.12 90.0 0.23 0.17 97.5 0.12 0.05 Protein 66.3 1.78 1.38 83.6 1.24 1.02 75.6 1.51 1.17 82.5 0.82 0.60 NaCl 91.8 0.19 0.15 94.0 0.17 0.087 92.5 0.19 0.12 95.1 0.15 0.042 Added Water 54.4 7.60 5.90 81.2 4.87 4.11 69.7 6.20 4.85 90.3 3.50 2.73

Comparison of PCR and ANN

85.0 25.0 5.0 Predicted Moisture Content (%) 80.0 75.0 70.0 65.0 Predicted Fat Content (%) 20.0 15.0 10.0 5.0 leg streaky ham unknown Predicted Ash Content (%) 4.5 4.0 3.5 3.0 2.5 60.0 60.0 65.0 70.0 75.0 80.0 85.0 0.0 0.0 5.0 10.0 15.0 20.0 25.0 2.0 2.0 2.5 3.0 3.5 4.0 4.5 5.0 Moisture Content (%) Fat Content (%) Ash Content (%) 25.0 3.0 40.0 Predicted Protein Content (%) 20.0 15.0 10.0 5.0 5.0 10.0 15.0 20.0 25.0 Predicted Salt Content (%) 2.5 2.0 1.5 1.5 2.0 2.5 3.0 Predicted Added Water (%) 30.0 20.0 10.0 0.0-10.0 0.0 10.0 20.0 30.0 40.0-10.0 Protein Content (%) Salt Content (%) Calculated Added Water (%)

85.0 25.0 5.0 Predicted Moisture Content (%) 80.0 75.0 70.0 65.0 Predicted Fat Content (%) 20.0 15.0 10.0 5.0 leg streaky ham unknown Predicted Ash Content (%) 4.5 4.0 3.5 3.0 2.5 60.0 60.0 65.0 70.0 75.0 80.0 85.0 Moisture Content (%) 0.0 0.0 5.0 10.0 15.0 20.0 25.0 Fat Content (%) 2.0 2.0 2.5 3.0 3.5 4.0 4.5 5.0 Ash Content (%) 25.0 3.0 40.0 Predicted Protein Content (%) 20.0 15.0 10.0 5.0 5.0 10.0 15.0 20.0 25.0 Predicted Salt Content (%) 2.5 2.0 1.5 1.5 2.0 2.5 3.0 Predicted Added Water (%) 30.0 20.0 10.0 0.0-10.0 0.0 10.0 20.0 30.0 40.0-10.0 Protein Content (%) Salt Content (%) Calculated Added Water (%)

Acknowledgements Some of the data used in this paper was taken from work supported by the UK Food Standards Agency. The authors are grateful for permission to use it. Further data was obtained from a project supported by the Commission of the European Communities, Agriculture and Fisheries (FAIR) specific RTD programme FAIR CT97-3020 Added Water in Foods. It does not necessarily reflect the Commission s views and in no way anticipates its future policy in this area.

2024 © cuisinedocbox.com Privacy Policy | Terms of Service | Feedback