Near-infrared spectroscopy and imaging in food quality and safety

Over the last two decades, near-infrared spectroscopy (NIRS) has established itself as a non-destructive analytical technique in a variety of disciplines. However, recent technological advancements in hardware design and data mining techniques have unleashed the potential of NIRS to become a tool of choice for routine analyses of agricultural products. The current paper synthesizes the status of NIRS in the agri-food industry in terms of hardware and software development as well as the direction in which the NIRS research is headed. An extensive review of literature reveals that the emphasis on hardware development is focused on developing compact, robust, and portable spectrometers and hyperspectral imaging (HSI) systems. The software development on the other hand is geared towards developing better preprocessing, analyses, and modeling techniques using chemometrics, support vector machines, and artificial neural networks. The four main agri-food sectors identified to be the beneficiaries of this research revolution are grain quality monitoring; post-harvest handling of fruits and vegetables; identification of contaminants in animal produce and feed; and food safety and authenticity. Apart from discussing the aforementioned topics, the paper also provides food scientists some working knowledge on parameters crucial to the performance of spectral and imaging systems. It is expected that further development of NIRS will help agricultural and food scientists to enhance the quality and safety of our food.

[1]  R. Lu,et al.  An lctf-based multispectral imaging system for estimation of apple fruit firmness: Part I. Acquisition and characterization of scattering images , 2006 .

[2]  Takayuki Kojima,et al.  Evaluation of pectin constituents of Japanese pear by near infrared spectroscopy , 2007 .

[3]  Paul Geladi,et al.  Hyperspectral imaging: calibration problems and solutions , 2004 .

[4]  Yukihiro Ozaki,et al.  Near-Infrared Spectroscopy in Food Science and Technology: Ozaki/Near-Infrared Spectroscopy in Food Science and Technology , 2006 .

[5]  Dietrich Wienke,et al.  On the use of recent developments in vibrational spectroscopic instrumentation in an industrial environment: quicker, smaller and more robust , 2000 .

[6]  A Comparison of Fourier and Wavelet Transforms in the Processing of near Infrared Spectroscopic Data: Part 1. Data Compression , 2003 .

[7]  C. Sandorfy,et al.  Principles of Molecular Vibrations for Near‐Infrared Spectroscopy , 2006 .

[8]  Junbin Gao,et al.  Chemometrics: From Basics to Wavelet Transform , 2004 .

[9]  C. S. French,et al.  The Analysis of Overlapping Spectral Absorption Bands by Derivative Spectrophotometry , 1955 .

[10]  W. R. Windham,et al.  Hyperspectral Imaging for Detecting Fecal and Ingesta Contaminants on Poultry Carcasses , 2002 .

[11]  D. O'shea,et al.  Elements of Modern Optical Design , 1985 .

[12]  P. Reyns,et al.  On-line measurement of grain quality with NIR technology , 2004 .

[13]  C. Creaser,et al.  Analytical applications of spectroscopy , 1988 .

[14]  L. Hoffman,et al.  Prediction of the chemical composition of freeze dried ostrich meat with near infrared reflectance spectroscopy. , 2005, Meat science.

[15]  Robert P. Cogdill,et al.  Least-Squares Support Vector Machines for Chemometrics: An Introduction and Evaluation , 2004 .

[16]  R. Hartmann,et al.  NIR determination of potato constituents , 1998, Potato Research.

[17]  M. Destain,et al.  Development of a multi-spectral vision system for the detection of defects on apples , 2005 .

[18]  Renfu Lu,et al.  AN LCTF-BASED MULTISPECTRAL IMAGING SYSTEM FOR ESTIMATION OF APPLE FRUIT FIRMNESS: PART II. SELECTION OF OPTIMAL WAVELENGTHS AND DEVELOPMENT OF PREDICTION MODELS , 2006 .

[19]  J. Roger,et al.  Non-destructive tests on the prediction of apple fruit flesh firmness and soluble solids content on tree and in shelf life , 2006 .

[20]  Lingzhi Zhao,et al.  Radial basis function neural networks in non-destructive determination of compound aspirin tablets on NIR spectroscopy. , 2006, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[21]  Jianwei Qin,et al.  Measurement of the Absorption and Scattering Properties of Turbid Liquid Foods Using Hyperspectral Imaging , 2007, Applied spectroscopy.

[22]  Moon S. Kim,et al.  Development of a Simple Algorithm for the Detection of Chilling Injury in Cucumbers from Visible/Near-Infrared Hyperspectral Imaging , 2005, Applied spectroscopy.

[23]  Paul Geladi,et al.  Hyperspectral NIR image regression part II: dataset preprocessing diagnostics , 2006 .

[24]  J. Chalmers,et al.  Handbook of vibrational spectroscopy , 2002 .

[25]  Márcio José Coelho Pontes,et al.  Classification of distilled alcoholic beverages and verification of adulteration by near infrared spectrometry , 2006 .

[26]  A. Peirs,et al.  Starch Index Determination of Apple Fruit by Means of a Hyperspectral near Infrared Reflectance Imaging System , 2003 .

[27]  Gerard Downey,et al.  Authentication of Food and Food Ingredients by near Infrared Spectroscopy , 1996 .

[28]  B. Kowalski,et al.  Partial least-squares regression: a tutorial , 1986 .

[29]  Sarah C. Rutan,et al.  Discovering chemistry with chemometrics , 1992 .

[30]  Marcelo Blanco,et al.  NIR spectroscopy: a rapid-response analytical tool , 2002 .

[31]  Chieu D. Tran Development and analytical applications of multispectral imaging techniques: An overview , 2001 .

[32]  Peter N. Schaare,et al.  A Versatile near Infrared Imaging Spectrometer , 1999 .

[33]  J. Callis,et al.  Direct Use of Second Derivatives in Curve-Fitting Procedures , 1989 .

[34]  D. Jennings,et al.  Deconvolution of diode-laser spectra , 1985 .

[35]  E. Loewen DIFFRACTION GRATING HANDBOOK , 1970 .

[36]  W. R. Windham,et al.  A Hyperspectral Imaging System for Identification of Faecal and Ingesta Contamination on Poultry Carcasses , 2003 .

[37]  Y. R. Chen,et al.  Principal component regression of near-infrared reflectance spectra for beef tenderness prediction , 2001 .

[38]  T. Næs,et al.  Multivariate strategies for classification based on NIR-spectra—with application to mayonnaise , 1999 .

[39]  Jon Gabrielsson,et al.  Recent Developments in Multivariate Calibration , 2006 .

[40]  C. Scotter,et al.  Determination of the Authenticity of Orange Juice by Discriminant Analysis of near Infrared Spectra , 1993 .

[41]  Bim Prasad Shrestha,et al.  Integrating multispectral reflectance and fluorescence imaging for defect detection on apples , 2006 .

[42]  Tormod Næs,et al.  Multivariate calibration. I. Concepts and distinctions , 1984 .

[43]  Moon S. Kim,et al.  A transportable fluorescence imagining system for detecting fecal contaminants , 2005 .

[44]  Paul Geladi,et al.  Chemometrics in spectroscopy : Part 2. Examples , 2004 .

[45]  Isabelle Noiseux,et al.  Simple Fiber-Optic-Based Sensors for Process Monitoring: An Application in Wine Quality Control Monitoring , 2004, Applied spectroscopy.

[46]  D. L. Wetzel Near-Infrared Reflectance Analysis , 1983 .

[47]  J. Warren Blaker,et al.  Optics: An Introduction for Students of Engineering , 1993 .

[48]  Robert G. Michel,et al.  A REVIEW OF RECENT APPLICATIONS OF NEAR INFRARED SPECTROSCOPY, AND OF THE CHARACTERISTICS OF A NOVEL PbS CCD ARRAY-BASED NEAR-INFRARED SPECTROMETER , 2002 .

[49]  K. Jetter,et al.  Quantitative analysis of near infrared spectra by wavelet coefficient regression using a genetic algorithm , 1999 .

[50]  P. Geladi,et al.  Linearization and Scatter-Correction for Near-Infrared Reflectance Spectra of Meat , 1985 .

[51]  Paul Geladi,et al.  Spectral Pre-Treatments of Hyperspectral near Infrared Images: Analysis of Diffuse Reflectance Scattering , 2007 .

[52]  J. Guthrie,et al.  Application of commercially available, low-cost, miniaturised NIR spectrometers to the assessment of the sugar content of intact fruit , 2000 .

[53]  R. Tkachuk,et al.  The Kubelka–Munk Equation: Some Practical Considerations , 1996 .

[54]  S. Prasher,et al.  Pork quality and marbling level assessment using a hyperspectral imaging system , 2007 .

[55]  Jitendra Paliwal,et al.  Spectral Data Compression and Analyses Techniques to Discriminate Wheat Classes , 2006 .

[56]  Jitendra Paliwal,et al.  Generalisation Performance of Artificial Neural Networks for Near Infrared Spectral Analysis , 2006 .

[57]  L. Buydens,et al.  Multivariate calibration with least-squares support vector machines. , 2004, Analytical chemistry.

[58]  T. Næs The design of calibration in near infra‐red reflectance analysis by clustering , 1987 .

[59]  W. Schapaugh,et al.  Classification of Fungal-Damaged Soybean Seeds Using Near-Infrared Spectroscopy , 2004 .

[60]  A. Garrido-Varo,et al.  Near-infrared reflectance spectroscopy (NIRS) for the mandatory labelling of compound feedingstuffs: chemical composition and open-declaration , 2004 .

[61]  Yankun Peng,et al.  Assessing Peach Firmness by Multi-Spectral Scattering , 2005 .

[62]  Yukihiro Ozaki,et al.  How Can We Unravel Complicated near Infrared Spectra?—Recent Progress in Spectral Analysis Methods for Resolution Enhancement and Band Assignments in the near Infrared Region , 2001 .

[63]  Robert B. Bilhorn,et al.  Charge Transfer Device Detectors for Analytical Optical Spectroscopy—Operation and Characteristics , 1987 .

[64]  G. Batten An Appreciation of the Contribution of NIR to Agriculture , 1998 .

[65]  N. Saito,et al.  Electronically Tunable-Laser Light Sources for near Infrared Spectroscopy , 2003 .

[66]  Paul Geladi,et al.  Chemometrics in spectroscopy. Part 1. Classical chemometrics , 2003 .

[67]  Junhong Liu,et al.  Single-Kernel Maize Analysis by Near-Infrared Hyperspectral Imaging , 2004 .

[68]  P. Williams,et al.  Near-Infrared Technology in the Agricultural and Food Industries , 1987 .

[69]  Carl A. Anderson,et al.  Efficient Spectroscopic Calibration Using Net Analyte Signal and Pure Component Projection Methods , 2005 .

[70]  Richard G. Brereton,et al.  Chemometrics: Data Analysis for the Laboratory and Chemical Plant , 2003 .

[71]  Paul Geladi,et al.  Hyperspectral NIR image regression part I: calibration and correction , 2005 .

[72]  C. K. Spillman,et al.  Near Infrared Reflectance Spectroscopy for Online Particle Size Analysis of Powders and Ground Materials , 2001 .

[73]  Ian A. Cowe,et al.  Performance of European Artificial Neural Network (ANN) Calibrations for Moisture and Protein in Cereals Using the Danish Near-Infrared Transmission (NIT) Network , 2001 .

[74]  Moon S. Kim,et al.  Automated detection of fecal contamination of apples based on multispectral fluorescence image fusion , 2005 .

[75]  Jianwei Qin,et al.  DETECTION OF PITS IN TART CHERRIES BY HYPERSPECTRAL TRANSMISSION IMAGING , 2005 .

[76]  Tom Fearn,et al.  Practical Nir Spectroscopy With Applications in Food and Beverage Analysis , 1993 .

[77]  T. Næs,et al.  The Effect of Multiplicative Scatter Correction (MSC) and Linearity Improvement in NIR Spectroscopy , 1988 .

[78]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[79]  Jianwei Qin,et al.  Hyperspectral diffuse reflectance imaging for rapid, noncontact measurement of the optical properties of turbid materials. , 2006, Applied optics.

[80]  S. A. Hale,et al.  Detection and quantification of species authenticity and adulteration in crabmeat using visible and near-infrared spectroscopy. , 2007, Journal of agricultural and food chemistry.

[81]  Tormod Næs,et al.  Selection of Samples for Calibration in Near-Infrared Spectroscopy. Part II: Selection Based on Spectral Measurements , 1990 .

[82]  S. Shackelford,et al.  Development of optimal protocol for visible and near-infrared reflectance spectroscopic evaluation of meat quality. , 2004, Meat science.

[83]  Yukihiro Ozaki,et al.  Near-infrared spectroscopy in food science and technology , 2007 .

[84]  Lijuan Xie,et al.  Combination and comparison of chemometrics methods for identification of transgenic tomatoes using visible and near-infrared diffuse transmittance technique , 2007 .

[85]  Tormod Næs,et al.  Related versions of the multiplicative scatter correction method for preprocessing spectroscopic data , 1995 .

[86]  E. Dereniak,et al.  Optical radiation detectors , 1984 .

[87]  Christina P. Bacon,et al.  Miniature spectroscopic instrumentation: Applications to biology and chemistry , 2004 .

[88]  H. Martens,et al.  Light scattering and light absorbance separated by extended multiplicative signal correction. application to near-infrared transmission analysis of powder mixtures. , 2003, Analytical chemistry.

[89]  J. R. Wilson,et al.  Optoelectronics, an introduction , 1984 .

[90]  D. Massart,et al.  Application of Wavelet Packet Transform in Pattern Recognition of Near-IR Data , 1996 .

[91]  Yun Xu,et al.  Support Vector Machines: A Recent Method for Classification in Chemometrics , 2006 .

[92]  W. Fred McClure,et al.  204 Years of near Infrared Technology: 1800–2003 , 2003 .

[93]  Yud-Ren Chen,et al.  Machine vision technology for agricultural applications , 2002 .

[94]  Paul M. Mather,et al.  Support vector machines for classification in remote sensing , 2005 .

[95]  P. Armstrong,et al.  COMPARISON OF DISPERSIVE AND FOURIER-TRANSFORM NIR INSTRUMENTS FOR MEASURING GRAIN AND FLOUR ATTRIBUTES , 2006 .

[96]  S. Delwiche,et al.  The Effect of Spectral Pre-Treatments on the Partial Least Squares Modelling of Agricultural Products , 2004 .

[97]  Kurt C. Lawrence,et al.  Performance of hyperspectral imaging system for poultry surface fecal contaminant detection. , 2006 .

[98]  M. S. Kim,et al.  MULTISPECTRAL DETECTION OF FECAL CONTAMINATION ON APPLES BASED ON HYPERSPECTRAL IMAGERY: PART I. APPLICATION OF VISIBLE AND NEAR–INFRARED REFLECTANCE IMAGING , 2002 .

[99]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .

[100]  Chieu D. Tran,et al.  Infrared Multispectral Imaging: Principles and Instrumentation , 2003 .

[101]  Vincent Baeten,et al.  Combination of support vector machines (SVM) and near‐infrared (NIR) imaging spectroscopy for the detection of meat and bone meal (MBM) in compound feeds , 2004 .

[102]  A. Belousov,et al.  A flexible classification approach with optimal generalisation performance: support vector machines , 2002 .

[103]  Zhihui He,et al.  Determination of Tobacco Constituents with Acousto-Optic Tunable Filter-Near Infrared Spectroscopy , 2006 .

[104]  Jerry Workman,et al.  The state of multivariate thinking for scientists in industry: 1980–2000 , 2002 .

[105]  D. Massart Chemometrics: A Textbook , 1988 .

[106]  Fujitoshi Shinoki,et al.  Development of a Portable near Infrared Sugar-Measuring Instrument , 2002 .

[107]  Y. R. Chen,et al.  HYPERSPECTRAL REFLECTANCE AND FLUORESCENCE IMAGING SYSTEM FOR FOOD QUALITY AND SAFETY , 2001 .

[108]  Sumio Kawano,et al.  Useful Tips for Constructing a near Infrared-Based Quality Sorting System for Single Brown-Rice Kernels , 2004 .

[109]  D. Coomans,et al.  Recent developments in discriminant analysis on high dimensional spectral data , 1996 .

[110]  Ian Murray,et al.  Use of Discriminant Analysis on Visible and near Infrared Reflectance Spectra to Detect Adulteration of Fishmeal with Meat and Bone Meal , 2001 .

[111]  J. E. Guerrero,et al.  Implementation of LOCAL Algorithm with Near-Infrared Spectroscopy for Compliance Assurance in Compound Feedingstuffs , 2005, Applied spectroscopy.

[112]  Frank Vogt,et al.  Fast principal component analysis of large data sets based on information extraction , 2002 .

[113]  P. Butz,et al.  Recent Developments in Noninvasive Techniques for Fresh Fruit and Vegetable Internal Quality Analysis , 2006 .

[114]  David S. Jackson,et al.  Classification and prediction of maize hardness-associated properties using multivariate statistical analyses , 2005 .

[115]  C. Tran,et al.  Simultaneous multispectral imaging in the visible and near-infrared region: applications in document authentication and determination of chemical inhomogeneity of copolymers. , 1998, Analytical chemistry.

[116]  G. Hemke,et al.  Prediction of pork quality using visible/near-infrared reflectance spectroscopy. , 2006, Meat science.

[117]  Paul Geladi,et al.  Principal Component Analysis , 1987, Comprehensive Chemometrics.

[118]  Yong He,et al.  A new approach to discriminate varieties of tobacco using vis/near infrared spectra , 2007 .

[119]  William Herschel,et al.  Experiments on the Refrangibility of the Invisible Rays of the Sun. By William Herschel, LL. D. F. R. S. , 1800 .

[120]  T. Fearn Standardisation and Calibration Transfer for near Infrared Instruments: A Review , 2001 .

[121]  M. Kumagai,et al.  Application of a Portable near Infrared Spectrometer for the Manufacturing of Noodle Products , 2004 .

[122]  Alan M. Lefcourt,et al.  A novel integrated PCA and FLD method on hyperspectral image feature extraction for cucumber chilling damage inspection , 2004 .

[123]  R. Bonner,et al.  Application of wavelet transforms to experimental spectra : Smoothing, denoising, and data set compression , 1997 .

[124]  W. McClure,et al.  Hand-Held NIR Spectrometry. Part II: An Economical No-Moving Parts Spectrometer for Measuring Chlorophyll and Moisture , 2002 .

[125]  Design and evaluation of a visible-to-near-infrared electronic slitless spectrograph , 2006 .

[126]  M. Ngadi,et al.  Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry , 2007 .

[127]  Tormod Næs,et al.  Multivariate calibration. II. Chemometric methods , 1984 .

[128]  Carlos Miralbés Prediction chemical composition and alveograph parameters on wheat by near-infrared transmittance spectroscopy. , 2003, Journal of agricultural and food chemistry.

[129]  Yong He,et al.  Discrimination of varieties of tea using near infrared spectroscopy by principal component analysis and BP model , 2007 .

[130]  Charles R. Hurburgh,et al.  Dimensionality Reduction of near Infrared Spectral Data Using Global and Local Implementations of Principal Component Analysis for Neural Network Calibrations , 2007 .

[131]  Vincent Baeten,et al.  New developments in the detection and identification of processed animal proteins in feeds , 2007 .

[132]  Floyd E. Dowell,et al.  AUTOMATED DETECTION OF SINGLE WHEAT KERNELS CONTAINING LIVE OR DEAD INSECTS USING NEAR–INFRARED REFLECTANCE SPECTROSCOPY , 2003 .

[133]  C. N. Thai,et al.  DEVELOPMENT OF A SPECTRAL IMAGING SYSTEM BASED ON A LIQUID CRYSTAL TUNABLE FILTE , 1998 .

[134]  Bart Nicolai,et al.  Non-destructive measurement of bitter pit in apple fruit using NIR hyperspectral imaging , 2006 .

[135]  H. Martens,et al.  Near-Infrared Absorption and Scattering Separated by Extended Inverted Signal Correction (EISC): Analysis of Near-Infrared Transmittance Spectra of Single Wheat Seeds , 2002 .

[136]  V. A. McGlone,et al.  Prediction of storage disorders of kiwifruit (Actinidia chinensis) based on visible-NIR spectral characteristics at harvest , 2004 .

[137]  Jerry Workman Review of Process and Non-invasive Near-Infrared and Infrared Spectroscopy: 1993-1999 , 1999 .

[138]  I. Young,et al.  Calibration and Characterisation of Imaging Spectrographs , 2003 .

[139]  土川 覚 Useful and advanced information in the field of near infrared spectroscopy , 2003 .

[140]  M Smith,et al.  Near infrared spectroscopy. , 1999, British journal of anaesthesia.

[141]  Gerard Downey,et al.  Prediction of Tenderness and other Quality Attributes of Beef by near Infrared Reflectance Spectroscopy between 750 and 1100 nm; Further Studies , 2001 .

[142]  Lutgarde M. C. Buydens,et al.  Clustering multispectral images: a tutorial , 2005 .

[143]  Renfu Lu,et al.  Visible and near-infrared spectroscopy for nondestructive quality assessment of pickling cucumbers , 2007 .

[144]  Li Wang,et al.  Feasibility study of quantifying and discriminating soybean oil adulteration in camellia oils by attenuated total reflectance MIR and fiber optic diffuse reflectance NIR , 2006 .

[145]  R. Barnes,et al.  Standard Normal Variate Transformation and De-Trending of Near-Infrared Diffuse Reflectance Spectra , 1989 .

[146]  John P. Coates,et al.  Vibrational Spectroscopy: Instrumentation for Infrared and Raman Spectroscopy∗ , 1998 .

[147]  A Photodiode-Array-Based Near-Infrared Spectrophotometer for the 600–1100 nm Wavelength Region , 1989 .

[148]  S. Lowry,et al.  Determination of Wavelength Accuracy in the Near-Infrared Spectral Region Based on NIST's Infrared Transmission Wavelength Standard SRM 1921 , 2000 .

[149]  D. R. Massie,et al.  Spectral Reflectance and Transmittance Properties of Grain in the Visible and Near Infrared , 1965 .

[150]  W. R. Windham,et al.  CALIBRATION OF A PUSHBROOM HYPERSPECTRAL IMAGING SYSTEM FOR AGRICULTURAL INSPECTION , 2003 .

[151]  Emil W. Ciurczak,et al.  Handbook of Near-Infrared Analysis , 1992 .

[152]  B. Overholt,et al.  An AOTF-based dual-modality hyperspectral imaging system (DMHSI) capable of simultaneous fluorescence and reflectance imaging. , 2006, Medical engineering & physics.

[153]  P Dardenne,et al.  Classification of modified starches by fourier transform infrared spectroscopy using support vector machines. , 2005, Journal of agricultural and food chemistry.

[154]  J. Roger,et al.  Application of LS-SVM to non-linear phenomena in NIR spectroscopy: development of a robust and portable sensor for acidity prediction in grapes , 2004 .

[155]  Tormod Næs,et al.  Selection of Samples for Calibration in Near-Infrared Spectroscopy. Part I: General Principles Illustrated by Example , 1989 .

[156]  Floyd E. Dowell,et al.  Detection of insect fragments in wheat flour by near-infrared spectroscopy $ , 2003 .

[157]  Chieu D. Tran,et al.  Principles, Instrumentation, and Applications of Infrared Multispectral Imaging, An Overview , 2005 .