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Quantitative Methods for Food Safety and Quality in the Vegetable Industry pp 113–159Cite as

Quality of Vegetable Products: Assessment of Physical, Chemical, and Microbiological Changes in Vegetable Products by Nondestructive Methods

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Part of the book series: Food Microbiology and Food Safety ((PRACT))

Abstract

This chapter focuses on quality and safety evaluation of the vegetable products using different sensing technologies, imaging processing, and chemometric methods. It provides an overview of the instruments used for evaluating the quality of vegetable products such as computer vision, multispectral imaging, near-infrared spectroscopy, and hyperspectral imaging (refer to Sect. 2). Then, the basic analysis methods and chemometrics are introduced in detail (Sect. 3), including image/spectral preprocessing and correction/calibration, feature and band extraction and sample selection, and analysis models and evaluation. Finally, the potential applications of the instruments and the basic analysis methods in vegetable product quality and safety analysis and control are explained (Sect. 4). The external qualities such as shape, size, color, texture, and defects; internal qualities such as soluble solid content (SSC), acid content, and internal defects; and microbiological changes such as microbial and fecal contamination are discussed in detail. Conclusions and future works are proposed (Sect. 5).

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Abbreviations

ANN:

Artificial neural network

ATR:

Attenuated total reflectance

AVIRIS:

Airborne visible/infrared imaging spectrometer

BD:

Band difference

BiPLS/FiPLS:

Backward/forward interval partial least squares

BR:

Band ratio

CARS:

Competitive adaptive reweighted sampling

CCD:

Charge-coupled device

CVS:

Computer vision system

DA:

Discriminant analysis

DCT:

Discrete cosine transform

DFT:

Discrete Fourier transform

DPLS:

Discriminant partial least squares

FTIR:

Fourier transform infrared

FTNIR:

Fourier transform near-infrared

GA:

Genetic algorithms

GAiPLS:

Genetic algorithm interval partial least squares

GAP:

Good agricultural practices

GMP:

Good manufacturing practices

HACCP:

Hazard analysis of critical control points

HIS:

Hyperspectral imaging system

HSI:

Hue, saturation, and intensity

ICA:

Independent component analysis

iPLS:

Interval partial least squares

ISO:

International Organization for Standardization

KNA:

Kernel nonlinear analysis

KNN:

Kernel neural network

KS:

Kennard–Stone

LDA:

Linear discriminant analysis

LS-SVM:

Least squares support vector machine

LV:

Latent variable

MLP:

Multilayer perceptron

MLR:

Multiple linear regression

MSC:

Multiplicative scatter correction

MWPLSR:

Moving window partial least squares regression

NASA:

National Aeronautics and Space Administration

NIRS:

Near-infrared spectroscopy

OSC:

Orthogonal signal correction

PCA:

Principal component analysis

PCR:

Principal component regression

PLS:

Partial least squares

PLSDA:

Partial least squares discriminant analysis

PLSR:

Partial least squares regression

r:

Correlation coefficient

RBF:

Radial basis function

RGB:

Red, green, blue

RMSEC:

Root mean square error of calibration

RMSECV:

Root mean square error of cross-validation

RMSEP:

Root mean square error of prediction

RMSEV:

Root mean square error of validation

ROC:

Receiver operating characteristic

RPD:

Residual predictive deviation

RS:

Random sampling

RSD:

Relative standard deviation

RT:

Regression trees

SAA:

Simulated annealing algorithm

SEC:

Standard error of calibration

SECV:

Standard error of cross-validation

SEM:

Scanning electron microscopy

SEP:

Standard error of prediction

SEV:

Standard error of validation

SID:

Spectral information divergence

SIMCA:

Soft independent modeling of class analogy

siPLS:

Synergy interval partial least squares

SNV:

Standard normal variate

SPA:

Successive projection algorithm

SPXY:

Sample set partitioning based on joint x–y distances

SSC:

Soluble solids content

UV:

Ultraviolet

UVE:

Elimination of uninformative variables

VIP:

Variable importance in projection

Vis/NIR:

Visible and near-infrared

WT:

Wavelet transformation

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Author information

Authors and Affiliations

  1. Beijing Research Center for Information Technology in Agriculture, Beijing, China

    Wenqian Huang, Jiangbo Li, Baohua Zhang & Shuxiang Fan

  2. Beijing Research Center of Intelligent Equipment for Agriculture, Beijing, China

    Wenqian Huang, Jiangbo Li, Baohua Zhang & Shuxiang Fan

  3. National Engineering Research Center for Information Technology in Agriculture, Beijing, China

    Wenqian Huang, Jiangbo Li, Baohua Zhang & Shuxiang Fan

  4. National Research Center of Intelligent Equipment for Agriculture, Beijing, China

    Wenqian Huang, Jiangbo Li, Baohua Zhang & Shuxiang Fan

Authors
  1. Wenqian Huang
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  2. Jiangbo Li
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  3. Baohua Zhang
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  4. Shuxiang Fan
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Corresponding author

Correspondence to Wenqian Huang .

Editor information

Editors and Affiliations

  1. Department of Food Science and Technology, International Campus of Excellence in the AgriFood Sector (CeiA3), University of Cordoba, Córdoba, Spain

    Fernando Pérez-Rodríguez

  2. Food Science and Human Nutrition, Agricultural University of Athens, Athens, Greece

    Panagiotis Skandamis

  3. Food Sciences and Nutrition, University of Malta, Msida, Malta

    Vasilis Valdramidis

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Huang, W., Li, J., Zhang, B., Fan, S. (2018). Quality of Vegetable Products: Assessment of Physical, Chemical, and Microbiological Changes in Vegetable Products by Nondestructive Methods. In: Pérez-Rodríguez, F., Skandamis, P., Valdramidis, V. (eds) Quantitative Methods for Food Safety and Quality in the Vegetable Industry. Food Microbiology and Food Safety(). Springer, Cham. https://doi.org/10.1007/978-3-319-68177-1_6

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