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Novel Sensing Technologies During the Food Drying Process

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Abstract

Instantly sensing the food quality attributes is a prerequisite to regulate the drying process for final product quality improvement. Novel sensing technologies have been “pushed” to food drying engineering from other research disciplines. Electronic nose, computer vision, hyperspectral imaging, near-infrared, nuclear magnetic resonance, and dielectric properties have been “transplanted” for this purpose. Various physicochemical attributes of food quality, including flavors, moisture contents, colors, shapes, texture, and chemical contents, have been sensed during the food drying process. Sun and solar drying, hot air convective drying, freeze drying, and microwave drying have been involved in these attempts. Numerous data processing and analysis techniques have been employed to make clear the meaning of multivariate variables for the above technologies. With the assistance of these technologies, a great diversity of drying processes have been modulated and food product quality had been improved. To promote their industrial application, more research is required to be conducted and more practical sensing method development is suggested along this direction.

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Abbreviations

ANN:

Artificial neural networks

AOTF-NIR:

Acousto-optic tunable filter-near-infrared

BPNN:

Backpropagation neural network

CV:

Computer vision

DS:

Dielectric spectroscopy

EM:

Electromagnetic

E-nose:

Electronic nose

FBRM:

Focused beam reflectance measurement

FDA:

Fisher discriminant analysis

FID:

Flame ionization detector

FL:

Fuzzy logic

FT-NIR :

Fourier transform NIR

GLCM:

Gray-level co-occurrence matrix

HSI:

Hyperspectral imaging

HS-SPME:

Headspace solid phase micro-extraction

LSD:

Least significant difference

LS-SVM:

Least squares support vector machines

MLR:

Multiple linear regression

MNF:

Minimum noise fraction

MOS:

Metal oxide semiconductor

MSC:

Multiple scattering correction

NIR:

Near-infrared spectroscopy

NMR:

Nuclear magnetic resonance

PAT:

Process analytical technology

PCA:

Principal component analysis

PCR:

Principal component regression

PLS:

Partial least squares

PEPT:

Positron emission particle tracking

PLS-DA:

Partial least squares discriminant analysis

PLSR:

Partial least square regression

QMB:

Quartz microbalance

ROI:

Region of interest

SFV:

Spatial filter velocimetry

SL:

Statistical learning

SNV:

Standard normal variate

TS-HSI:

Time series hyperspectral imaging

VOCs:

Volatile organic compounds

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Funding

The authors would like to extend their appreciation for the financial support provided by the Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment & Technology.

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Authors and Affiliations

  1. Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment & Technology, School of Mechanical Engineering, Jiangnan University, 1800 Lihu Avenue, Wuxi, 214122, Jiangsu, China

    Jing Li, Zhenfeng Li, Yongsheng Pei, Chunfang Song & Guanyu Zhu

  2. Division of Agricultural Sciences and Natural Resources, Department of Biosystems and Agricultural Engineering, Oklahoma State University, 111 Ag Hall, Stillwater, OK, 74078, USA

    Ning Wang

  3. Department of Bioresource Engineering, McGill University, 21111 Lakeshore Road, Ste-Anne-de-Bellevue, QC, H9X 3V9, Canada

    G. S. V. Raghavan

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  1. Jing Li
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  2. Zhenfeng Li
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  3. Ning Wang
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  4. G. S. V. Raghavan
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  5. Yongsheng Pei
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  6. Chunfang Song
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  7. Guanyu Zhu
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Correspondence to Zhenfeng Li.

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Li, J., Li, Z., Wang, N. et al. Novel Sensing Technologies During the Food Drying Process. Food Eng Rev 12, 121–148 (2020). https://doi.org/10.1007/s12393-020-09215-2

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