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Wavelet Analysis of Signals in Agriculture and Food Quality Inspection

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Abstract

Food quality and safety have become the top priorities for agriculture and food processing industry due to the increasing consumer demand for high-quality healthy food. The food processing industry is currently focusing on using fast, precise, and nondestructive automated quality inspection techniques. Near-infrared spectroscopy, image processing, hyperspectral imaging, X-rays, and ultrasonic techniques have been researched and shown to have high potential for automated inspection. The biggest challenge in the automated inspection systems deals with signal pre-processing, denoising, feature extraction, and its re-synthesis for classification purposes. Several research studies have established that the technique of wavelet analysis can very well resolve these issues of signal processing in many systems used for quality inspection of agricultural and food products. The objective of this paper is to discuss the theory of wavelet analysis and review its application in signal processing and feature extraction for quality monitoring of agricultural and food products.

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References

  • Aboufadel, E., & Chlicker, S. (1999). Discovering wavelets. New York, USA: John Wiley & Sons Inc.

    Google Scholar 

  • Anastasia, T., Maenza, G., & Polikar, R. (2002). Wavelet packets as a means of searching for weak narrow band signals. In: Proceedings of 4th IASTED International Conference on Signal and Image Processing, 12–14 August 2002. Kauai, USA.

  • Ayaz, E., Ozturk, A., & Seker, S. (2006). Continuous wavelet transform for bearing damage detection in electric motors. In: Proceedings of the 13th IEEE Mediterrnean Electrotechnical Conference, pp. 1130–1133, 16–19 May 2006, Terrmolinos, Spain.

  • Barclay, V. J., Bonner, R. F., & Hamilton, I. P. (1997). Application of wavelet transforms to experimental spectra: smoothing, denoising, and data set compression. Analytical Chemistry, 69, 78–90.

    Article  CAS  Google Scholar 

  • Blanco, S., Figliola, A., Quiroga, R. Q., Rosso, O. A., & Serrano, E. (1998). Time–Frequency analysis of electroencephalogram series. III. Wavelet packets and information cost function. Physical Review, 57(1), 932–940.

    CAS  Google Scholar 

  • Borah, S., Hines, E. L., & Bhuyan, M. (2007). Wavelet transform based image texture analysis for size estimation applied to the sorting of tea granules. Journal of Food Engineering, 79, 629–639.

    Article  Google Scholar 

  • Chang, T., & Kuo, C. C. J. (1993). Texture analysis and classification with tree-structured wavelet transform. IEEE Transactions on Image Processing, 2(4), 429–441.

    Article  CAS  Google Scholar 

  • Chen, B., Fu, X., & Lu, D. (2002). Improvement of predicting precision of oil content in instant noodles by using wavelet transforms to treat near-infrared spectroscopy. Journal of Food Engineering, 53, 373–376.

    Article  Google Scholar 

  • Chen, Z., & Tao, Y. (2001). Food safety inspection using “from presence to classification” object-detection model. Pattern Recognition, 34, 2331–2338.

    Article  Google Scholar 

  • Chen, C., & Zhang, J. (2007). Wavelet energy entropy as a new feature extractor for face recognition. In: Proceedings of Fourth International Conference on Image and Graphics, 22–24 August 2007, Los Alamitos, US.

  • Choudhary, R., Paliwal, J., & Jayas, D. S. (2008). Classification of cereal grains using wavelet, morphological, color, and textural features of non-touching kernel images. Biosystems Engineering, 99(3), 330–337.

    Article  Google Scholar 

  • Cocchi, M., Corbellini, M., Foca, G., Lucisano, M., Pagani, M. A., Tassi, L., et al. (2005). Classification of bread wheat flours in different quality categories by a wavelet-based feature selection/classification algorithm on NIR spectra. Analytica Chimica Acta, 544, 100–107.

    Article  CAS  Google Scholar 

  • Daubechies, I. (1992). Ten lectures on wavelets. Philadelphia, USA: Society for Industrial and Applied Mathematics.

    Google Scholar 

  • Du, C. J., & Sun, D. W. (2006). Correlating image texture features extracted by five different methods with the tenderness of cooked pork: A feasibility study. Transactions of the ASABE, 49(2), 441–448.

    Google Scholar 

  • El-Araby, E., El-Ghazawi, T., Moigne, J. L., & Gaj, K. (2004). Wavelet spectral dimension reduction of hyperspectral imagery on a reconfigurable computer. In: Proceedings of IEEE International Conference on Field-Programmable Technology, 6–8 December 2004, Brisbane, Australia.

  • Fu, X., Yan, G., Chen, B., & Li, H. (2005). Application of wavelet transforms to improve prediction precision of near infrared spectra. Journal of Food Engineering, 69, 461–466.

    Article  Google Scholar 

  • Gonzalez, R. C., & Woods, R. E. (2002). Digital image processing. Singapore: Pearson Education.

    Google Scholar 

  • Graps, A. (1995). An introduction to wavelets. IEEE Computational Sciences and Engineering, 2(2), 50–61.

    Article  Google Scholar 

  • Gributs, C. E. W., & Burns, D. H. (2006). Parsimonious calibration models for near-infrared spectroscopy using wavelets and scaling functions. Chemometrics and Intelligent Laboratory Systems, 83, 44–53.

    Article  CAS  Google Scholar 

  • Hambaba, A., & Huff, E. (2000). Multiresolution error detection on early fatigue cracks in gears. In: IEEE Aerospace Conference Proceedings, vol. 6, pp. 367–372, 18–25 March 2000, Montana, USA.

  • He, Y., Li, X., & Deng, X. (2007). Discrimination of varieties of tea using near infrared spectroscopy by principal component analysis and BP model. Journal of Food Engineering, 79(4), 1238–1242.

    Article  Google Scholar 

  • Huang, Y., Lacey, R. E., Moore, L. L., Miller, R. K., Whittaker, A. D., & Ophir, J. (1997). Wavelet textural features from ultrasonic elastograms for meat quality prediction. Transactions of the ASAE, 40(6), 1741–1748.

    Google Scholar 

  • Huang, Y., Lacey, R. E., & Whittaker, A. D. (1998). Neural network prediction model based on elastographic textural features for meat quality evaluation. Transactions of the ASAE, 41(4), 1173–1179.

    Google Scholar 

  • Jacques, G., Frymiare, J., Kounios, J., Clark, C., & Polikar, R. (2004). Multiresolution analysis for early diagnosis of Alzheimer’s disease. In: Proceedings of 26th Annual International Conference of IEEE Engineering in Medicine and Biology Society, pp. 251–254, 1–4 September 2004, San Francisco, USA.

  • Jacques, G., Frymiare, J. L., Kounios, J., Clark, C., & Polikar, R. (2005). Multiresolution wavelet analysis and ensemble of classifiers for early diagnosis of Alzheimer’s disease. In: Proceedings of IEEE International Conference on Acoustic, Speech, and Signal Processing, vol.5, pp. 389–392, 18–23 March 2005, Philadelphia, USA.

  • Jetter, K., Depczynski, U., Molt, K., & Niemoller, A. (2000). Principles and applications of wavelet transformation to chemometrics. Analytica Chimica Acta, 420, 169–180.

    Article  CAS  Google Scholar 

  • Karunakaran, C., Jayas, D. S., & White, N. D. G. (2004). Identification of wheat kernels damaged by red flour beetle using x–ray images. Biosystems Engineering, 87(3), 267–274.

    Article  Google Scholar 

  • Kong, S. G., Chen, Y. R., Kim, I., & Kim, M. S. (2004). Analysis of hyperspectral fluorescence images for poultry skin tumor inspection. Applied Optics, 43(4), 824–833.

    Article  Google Scholar 

  • Labat, D. (2005). Recent advances in wavelet analyses: Part 1. A review of concepts. Journal of Hydrology, 314, 275–288.

    Article  Google Scholar 

  • Labat, D., Ronchail, J., & Guyot, J. L. (2005). Recent advances in wavelet analyses: Part 2- Amazon, Parana, Orinoco, and Congo discharges time scale variability. Journal of Hydrology, 314, 289–311.

    Article  Google Scholar 

  • Li, X., He, Y., Wu, C., & Sun, D. (2007). Nondestructive measurement and fingerprint analysis of soluble solid content of tea soft drink based on Vis/NIR spectroscopy. Journal of Food Engineering, 82(3), 316–323.

    Article  CAS  Google Scholar 

  • Mallat, S. G. (1989). A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), 674–693.

    Article  Google Scholar 

  • Marchant, B. P. (2003). Time frequency analysis for biosystems engineering. Biosystems Engineering, 85(3), 261–281.

    Article  Google Scholar 

  • Matsuyama, A., & Jonkman, M. (2005). The Application of wavelet and feature vectors to ECG signals. In: Proceedings of the IEEE International Region 10 Conference, 21–24 November 2005, Melbourne, Australia.

  • Misti, M., Misti, Y., Oppenheim, G., & Poggi, J. M. (1996). Wavelet toolbox user’s guide. Massachusetts, USA: Maths Works Inc.

    Google Scholar 

  • Neethirajan, S., Jayas, D. S., & White, N. D. G. (2007). Detection of sprouted wheat kernels using soft X-ray image analysis. Journal of Food Engineering, 81, 509–513.

    Article  Google Scholar 

  • Nicolai, B. M., Theron, K. I., & Lammerty, J. (2007). Kernel PLS regression on wavelet transformed NIR spectra for prediction of sugar content of apple. Chemometrics and Intelligent Laboratory Systems, 85, 243–252.

    Article  CAS  Google Scholar 

  • Polikar, R. (1999). The story of wavelets. In N. Mastorakis (Ed.), Physics and modern topics in mechanical and electrical engineering (pp. 192–197). Wisconsin, USA: World Scientific and Engineering Society Press.

    Google Scholar 

  • Polikar, R., Keinert, F., & Greer, M. H. (2001). Wavelet analysis of event related potentials for early diagnosis of Alzheimer’s disease. In A. Petrosian, & F. G. Meyer (Eds.), Wavelets in signal and image analysis, from theory to practice (pp. 453–478). Boston, USA: Kluwer Academic Publishers.

    Google Scholar 

  • Polikar, R., Topalisa, A., Greenb, D., Kouniosb, J., & Clark, C. M. (2007). Comparative multiresolution wavelet analysis of ERP spectral bands using an ensemble of classifiers approach for early diagnosis of Alzheimer’s disease. Computers in Biology and Medicine, 37, 542–558.

    Article  Google Scholar 

  • Polikar, R., Udpa, L., Udpa, S. S., & Taylor, T. (1998). Frequency invariant classification of ultrasonic weld inspection signals. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 45(3), 614–625.

    Article  CAS  Google Scholar 

  • Rioul, O., & Vetterli, M. (1991). Wavelets and signal processing. IEEE Signal Processing Magazine, 8(4), 14–38.

    Article  Google Scholar 

  • Safavian, L. S., Kinsner, W., & Turanli, H. (2005). A quantitative comparison of different mother wavelets for characterizing transients in power systems. In: Canadian Conference on Electrical and Computer Engineering, 1–4 May 2005, Saskatoon, Canada.

  • Semler, L., Dettori, L., & Furst, J. (2005). Wavelet-based texture classification of tissues in computed tomography. In: Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems, 23-24 June 2005, Dublin, Ireland.

  • Simone, G., Morabito, F. C., Polikar, R., Ramuhalli, P., Udpa, L., & Udpa, S. S. (2001). Feature extraction techniques for ultrasonic signal classification. International Journal of Applied Electromagnetics and Mechanics, 15(1–4), 291–294.

    Google Scholar 

  • Tan, H.-W., & Brown, S. D. (2002). Wavelet analysis applied to removing non-constant, varying spectroscopic background in multivariate calibration. Journal of Chemometrics, 16(5), 228–240.

    Article  CAS  Google Scholar 

  • Tan, C., & Li, M. (2007). Calibration transfer between two near-infrared spectrometers based on a wavelet packet transform. Analytical Sciences, 23, 201–206.

    Article  CAS  Google Scholar 

  • Tao, J., Feng, S., You, H., Changwen, Q., & Rongjian, Q. (2007). Optimal wavelets vanishing moments signal detection. In: 8th International Conference on Electronic Measurement and Instruments, vol. 3, pp. 782–386, 16–18 August, 2007, Xi’an, China.

  • Trygg, J., & Wold, S. (1998). PLS regression on wavelet compressed NIR spectra. Chemometrics and Intelligent Laboratory Systems, 42, 209–220.

    Article  CAS  Google Scholar 

  • Unser, M. (1995). Texture classification and segmentation using wavelet frames. IEEE Transactions on Image Processing, 4(11), 1549–1560.

    Article  CAS  Google Scholar 

  • Valens, C. (1999). A really friendly guide to wavelets. Available at: http://pagesperso-orange.fr/polyvalens/clemens/download/arfgtw.pdf. Accessed 21 March 2008.

  • Walczak, B., Bouveresse, E., & Massart, D. L. (1997). Standardization of near-infrared spectra in the wavelet domain. Chemomenics and Intelligent Laboratory Systems, 36, 41–51.

    Article  CAS  Google Scholar 

  • Walker, J. S. (1998). A primer on wavelets and their scientific applications. Florida, USA: CRC Press.

    Google Scholar 

  • Wang, W., & Paliwal, J. (2006). Spectral data compression and analyses techniques to discriminate wheat classes. Transactions of the ASABE, 49(5), 1607–1612.

    Google Scholar 

  • Wang, W., & Paliwal, J. (2007). Near-infrared spectroscopy and imaging in food quality and safety. Sensing and Instrumentation in Food Quality, 1, 193–207.

    Article  Google Scholar 

  • Xu, C., Kim, I., & Kim, M. S. (2007). Poultry skin tumor detection in hyperspectral reflectance images by combining classifiers. In M. Kamel, & A. Campilho (Eds.), Image analysis and recognition (pp. 1289–1296). Berlin, Germany: Springer.

    Chapter  Google Scholar 

  • Zheng, C., Sun, D. W., & Zheng, L. (2006). Classification of tenderness of large cooked beef joints using wavelet and gabor textural features. Transactions of the ASABE, 49(5), 1447–1454.

    Google Scholar 

  • Zhu, B., Jiang, L., Luo, Y., & Tao, Y. (2007). Gabor feature-based apple quality inspection using kernel principal component analysis. Journal of Food Engineering, 81, 741–749.

    Article  Google Scholar 

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Acknowledgement

The authors thank the Natural Sciences and Engineering Research Council of Canada and the Canada Research Chairs program for funding this study.

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Authors and Affiliations

  1. Department of Biosystems Engineering, University of Manitoba, Winnipeg, MB, R3T 5V6, Canada

    Chandra B. Singh, Ruplal Choudhary, Digvir S. Jayas & Jitendra Paliwal

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  1. Chandra B. Singh
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  2. Ruplal Choudhary
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  3. Digvir S. Jayas
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  4. Jitendra Paliwal
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Correspondence to Digvir S. Jayas.

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Singh, C.B., Choudhary, R., Jayas, D.S. et al. Wavelet Analysis of Signals in Agriculture and Food Quality Inspection. Food Bioprocess Technol 3, 2–12 (2010). https://doi.org/10.1007/s11947-008-0093-7

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  • DOI: https://doi.org/10.1007/s11947-008-0093-7

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