000			04078cam a2200541Ii 4500
001			9781315209203
003			FlBoTFG
005			20220711212536.0
006			m o d
007			cr cnu|||unuuu
008			181126t20192019flu ob 001 0 eng d
040			_aOCoLC-P _beng _erda _epn _cOCoLC-P
020			_a9781351805940 _q(electronic bk.)
020			_a1351805940 _q(electronic bk.)
020			_a9781315209203 _q(electronic bk.)
020			_a1315209209 _q(electronic bk.)
020			_a9781351805957
020			_a1351805959
020			_a9781351805933
020			_a1351805932
020			_z9781138630796
035			_a(OCoLC)1076269166 _z(OCoLC)1070895154 _z(OCoLC)1077244600
035			_a(OCoLC-P)1076269166
050		4	_aTX547
072		7	_aTEC _x012000 _2bisacsh
072		7	_aTDCT _2bicssc
082	0	4	_a664.07 _223
245	0	0	_aHyperspectral imaging analysis and applications for food quality / _cedited by N.C. Basantia, Leo M.L. Nollet, Mohammed Kamruzzaman.
264		1	_aBoca Raton, FL : _bCRC Press, Taylor & Francis Group, _c[2019]
264		4	_c©2019
300			_a1 online resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	1		_aFood analysis and properties
520			_aIn processing food, hyperspectral imaging, combined with intelligent software, enables digital sorters (or optical sorters) to identify and remove defects and foreign material that are invisible to traditional camera and laser sorters. Hyperspectral Imaging Analysis and Applications for Food Quality explores the theoretical and practical issues associated with the development, analysis, and application of essential image processing algorithms in order to exploit hyperspectral imaging for food quality evaluations. It outlines strategies and essential image processing routines that are necessary for making the appropriate decision during detection, classification, identification, quantification, and/or prediction processes. Features Covers practical issues associated with the development, analysis, and application of essential image processing for food quality applications Surveys the breadth of different image processing approaches adopted over the years in attempting to implement hyperspectral imaging for food quality monitoring Explains the working principles of hyperspectral systems as well as the basic concept and structure of hyperspectral data Describes the different approaches used during image acquisition, data collection, and visualization The book is divided into three sections. Section I discusses the fundamentals of Imaging Systems: How can hyperspectral image cube acquisition be optimized? Also, two chapters deal with image segmentation, data extraction, and treatment. Seven chapters comprise Section II, which deals with Chemometrics. One explains the fundamentals of multivariate analysis and techniques while in six other chapters the reader will find information on and applications of a number of chemometric techniques: principal component analysis, partial least squares analysis, linear discriminant model, support vector machines, decision trees, and artificial neural networks. In the last section, Applications, numerous examples are given of applications of hyperspectral imaging systems in fish, meat, fruits, vegetables, medicinal herbs, dairy products, beverages, and food additives.
588			_aOCLC-licensed vendor bibliographic record.
650		0	_aHyperspectral imaging. _912157
650		0	_aFood _xAnalysis. _913631
650		0	_aFood _xQuality. _917458
650		7	_aTECHNOLOGY & ENGINEERING _xFood Science. _2bisacsh _98112
700	1		_aBasantia, N. C., _eeditor. _917459
700	1		_aNollet, Leo M. L., _d1948- _eeditor. _917460
700	1		_aKamruzzaman, Mohammed, _eeditor. _917461
856	4	0	_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9781315209203
856	4	2	_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
942			_cEBK
999			_c71532 _d71532