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Advanced Detection Techniques Using Artificial Intelligence in Processing of Berries

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Abstract

Berries are delicious and nutritious, making them among the popular fruits. There are various types of berries, the most common ones include blueberries, strawberries, raspberries, blackberries, grapes, and currants. Fresh berries combine high nutritional value and perishability. The processing of berries ensures high quality and enhanced marketability of the product. Sorting, disinfection, and decontamination are essential processes that many types of fruits such as citrus fruits, berries, pomes, and drupes must undergo to ensure improved quality, uniformity, and microbiological safety of the product. Drying and freezing are excellent processing methods to extend the shelf life of berries which also provide new options to the consumer of a wide variety of berries. With the demand for high quality and automatic high-throughput detection of the quality of fruit products, intelligent and rapid detection of various parameters during processing has become the development direction of modern food processing. Therefore, this paper reviews the application of advanced detection technologies, artificial intelligence-based methods for detection and prediction during berry sorting, drying, disinfecting, sterilizing, and freezing processing. These advanced detection techniques include computer vision system, near infrared, hyperspectral imaging, thermal imaging, low-field nuclear magnetic resonance, magnetic resonance imaging, electronic nose, and X-ray computed tomography. These artificial intelligence methods include mathematical modeling, chemometrics, machine learning, deep learning, and artificial neural networks. In general, advanced detection techniques incorporating artificial intelligence have not yet penetrated into all aspects of commercial berry processing, which include drying, disinfecting, sterilizing, and freezing processes.

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Abbreviations

ADE:

Advanced detection equipment

AI:

Artificial intelligence

ANFIS:

Adaptive neuro fuzzy inference system

ANN:

Artificial neural network

BPNN:

Back-propagation neural network

CARS:

Competitive adaptive reweighted sampling

CCD:

Charge-coupled device

CFBP:

Cascade forward back propagation

CFD:

Computational fluid dynamics

CFS:

Correlation-based feature selection subset

CNN:

Convolutional neural network

CONS:

Consistency subset

CT:

Computed tomography

CVS:

Computer vision system

DAE:

Deep autoencoder

DSC:

Differential scanning calorimetry

DT:

Decision tree

E-nose:

Electronic nose

FCN:

Fully convolutional network

FEM:

Finite element modeling

FFBP:

Feed forward back propagation

FL:

Fuzzy logic

FNN:

Feedforward neural network

FTIR:

Fourier transform infrared spectroscopy

GAN:

Generative adversarial network

GGCM:

Gray level-gradient co-occurrence matrix

GLCM:

Gray level co-occurrence matrix

HSI:

Hyperspectral imaging

iPLSR:

Interval partial least squares regression

KNN:

K-nearest neighbors

LDA:

Linear discriminant analysis

LF-NMR:

Low-field nuclear magnetic resonance

LIBS:

Laser-induced breakdown spectroscopy

LR:

Linear regression

LS-SVM:

Least-squares support vector machine

LS-SVR:

Least-squares support vector regression

LWR:

Locally weighted regression

MLP:

Multi-layer perceptron

MRAFC:

Model reference adaptive fuzzy control

MRI:

Magnetic resonance imaging

NIR:

Near infrared

OES:

Optical emission spectrometry

PCA:

Principal components analysis

PLS:

Partial least squares

PLSR:

Partial least squares regression

PNN:

Probabilistic neural network

RBFNN:

Radial basis function neural network

RF:

Random forest

RFR:

Random forest regression

RMSE:

Root mean square error

RNN:

Recurrent neural network

RPD:

Residual prediction deviation

RT-qPCR:

Real-time quantitative PCR

SMO:

Sequential minimal optimization

SPA:

Successive projection algorithm

SSC:

Soluble solid content

SVM:

Support vector machine

SVR:

Support vector regression

UVE:

Uninformative variable elimination

VC:

Vitamin C

WT:

Wavelet transform

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Acknowledgements

We acknowledge financial supports from the National Key R&D Program of China (Contract No. 2017YFD0400901), Jiangsu Province Key Laboratory Project of Advanced Food Manufacturing Equipment and Technology (No. FMZ202003), and Special Funds for Taishan Industry Leading Talents Project, all of which enabled us to carry out this study.

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Authors and Affiliations

  1. State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu Province, 214122, China

    Dayuan Wang & Min Zhang

  2. International Joint Laboratory On Food Safety, Jiangnan University, Wuxi, Jiangsu Province, 214122, China

    Dayuan Wang

  3. Jiangsu Province Key Laboratory of Advanced Food Manufacturing Equipment and Technology, Jiangnan University, Wuxi, Jiangsu Province, 214122, China

    Min Zhang

  4. Department of Bioresource Engineering, Macdonald Campus, McGill University, Montreal, QC, Canada

    Arun S. Mujumdar

  5. Shanghao Biotech Co., Ltd, Qingdao, Shandong, 266700, China

    Dongxing Yu

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  1. Dayuan Wang
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Correspondence to Min Zhang.

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Wang, D., Zhang, M., Mujumdar, A.S. et al. Advanced Detection Techniques Using Artificial Intelligence in Processing of Berries. Food Eng Rev 14, 176–199 (2022). https://doi.org/10.1007/s12393-021-09298-5

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