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

  • Wenqian Huang
  • Jiangbo Li
  • Baohua Zhang
  • Shuxiang Fan
Chapter
Part of the Food Microbiology and Food Safety book series (FMFS)

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).

Keywords

Vegetable products Quality Safety Computer vision Near-infrared spectroscopy Spectral imaging Chemometrics 

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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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Wenqian Huang
    • 1
    • 2
    • 3
    • 4
  • Jiangbo Li
    • 1
    • 2
    • 3
    • 4
  • Baohua Zhang
    • 1
    • 2
    • 3
    • 4
  • Shuxiang Fan
    • 1
    • 2
    • 3
    • 4
  1. 1.Beijing Research Center for Information Technology in AgricultureBeijingChina
  2. 2.Beijing Research Center of Intelligent Equipment for AgricultureBeijingChina
  3. 3.National Engineering Research Center for Information Technology in AgricultureBeijingChina
  4. 4.National Research Center of Intelligent Equipment for AgricultureBeijingChina

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