Evaluation of biological contaminants in foods by hyperspectral imaging: A review

Figures & data

Figure 1. Main parameters controlled in food production.

Table 1. Some of most common analytical techniques for detecting biological contaminants in foods.

Analytical technique	Contaminant	Food	Reference
Plate count	Escherichia coli	Fresh plant-based foods	[20]
	Total bacteria	Beef	[74,145,146]
	Total bacteria	Chicken meat	[143]
	Total bacteria	Salmon and carp	[144,170,178]
	Total bacteria and PPC	Pork	[152]
	LAB	Salmon	[172]
	Pseudomonas, Brochothrix thermosphacta, and Enterobacteriaceae	Different meats	[101,141,142,180]
Microscopy	Bacteria and fungi	Different foods	[21]
ELISA	Fungal toxins	Cereals	[37,39]
	Bacterial toxins	Meat, sausage, milk, and mayonnaise	[38,40]
	Virus	Crops	[89,90]
	Anisakis simplex proteins	Sea products	[67,71]
	Trichinella	Pork	[68]
	Taenia saginata	Beef and pork	[69,70]
PCR	Virus	Crops	[90]
	Salmonella	Meats and milk	[197]
	Bacteria	Fruit crops	[198]
	Fungal toxins	Cereals	[199]
	Taenia saginata	Beef and pork	[66,70]
HPLC and TLC	Fungal toxins	Cereals	[41-43]
LC-MS	Anisakis simplex proteins	Fish	[72]
Detection by fluorescence	Fungal toxins	Cereals	[43,48-50]
Ultrasound	LAB, Aspergillus niger, and Saccharomyces cerevisiae	Fruit juices	[30]
	Parasites	Fish	[65]
Vis/IR spectroscopy	Microbial contamination	Fresh and processed foods	[34]
	Fungal contamination	Red pepper powder	[35]
Visual inspection	Parasites	Fish	[60,61]
MID-FTIR	Trichinella	Pork	[193]

ELISA, enzyme-linked immunosorbent assay; HPLC, high-performance liquid chromatography; LAB, lactic acid bacteria; LC-MS, liquid chromatography–mass spectrometry; MID-FTIR, mid-infrared Fourier transform spectroscopy; PCR, polymerase chain reaction; PPC, psychrotrophic plate count; TLC, thin layer chromatography.

Figure 2. Schematic of the hyperspectral imaging (HSI) system: (a) acquisition of hyperspectral image; (b) hypercube: spectral and special information in a hyperspectral image at different wavelengths; (c) spectral signature for each constituent of the sample plotted against different wavelengths.

Table 2. Spectral imaging applications for detecting microbial contamination in agricultural crops.

Cultivar	Contaminating agent	Aim	Wavelength (λ, nm)	Reference
Maize	Fungi: Helminthosporium maydis Virus: MDMV	Spectral differentiation in the NIR region (800–2600 nm) between healthy, MDMV infected, and H. maydis–infected leaves	400–2700	[81]
Wheat	Fungi: Fusarium culmorum	Correct classification between diseased and healthy ear tissues	400–1000	[84]
Strawberry	Fungi: Colletotrichum gloeosporioides	Early detection of infection stages of strawberry foliar anthracnose	400–1000	[80]
Citrus fruit	Bacteria: Xanthomonas axonopodis	Multispectral algorithms to differentiate “citrus canker” from other peel conditions	450–930	[85]
Grapevine	Virus: GLRaV-3	Evaluation of the reliability of hyperspectral airborne imaging to remotely detect GLRaV-3 vineyards	400–1000	[92]
		Correct classification between infected and healthy vine leaves	350–2500	[93]
Watermelon seeds	Virus: CGMMV	To discriminate virus-infected watermelon seeds from healthy ones	946–2016	[94]

MDMV, maize dwarf mosaic virus; GLRaV-3, grapevine leafroll-associated virus-3; CGMMV, cucumber green mottle mosaic virus.

Table 3. Hyperspectral imaging (HSI) for detecting microbial contamination and damages caused by insects and/or their organic residues in plant-based foods.

Contaminant	Food	Mode^a	Wavelength (λ, nm)	Accuracy	References
Microbial toxins
Aflatoxin	Maize	F	450–500	94.4%	[43]
	Maize	R	1100–1700	96.9%	[48]
	Maize	R	400–1000	98%	[106]
	Maize	R	550–1700	95%	[102]
	Maize	T	500–950	95%	[102]
Fumonisin	Milled maize	R	720–940	R^2 = 0.68	[42]
Ochratoxin A	Barley	R	1000–1600	100%	[98]
Viable microorganisms and diseases
Aspergillus and Fusarium	Maize	R	400–1000	97.0%, 94.5%	[45]
Aspergillus and Penicillium verrucosum	Barley	R	1000–1600	100%	[98]
Fusarium	Maize	A	1000–2498	R^2 = 0.98	[100]
Aspergillus niger	Wheat	R	1000–1600	92.9%	[112]
Aspergillus glaucus	Wheat	R	1000–1600	87.2%	[112]
Penicillium	Wheat	R	1000–1600	99.3%	[112]
Aspergillus oryzae	Rice	R	400–1000	R^2 = 0.97	[118]
Decay	Citrus fruit	R	325–1100	98.6%	[130]
		R	460–1020	87.5–95.5%	[131]
Canker	Citrus fruit	R	450–930	95.7%	[85]
		R	450–930	93.3–96.7%	[86]
		R	730 and 830	95.3%	[87]
Fecal contamination	Apple	F and R	400–1000	100% and 99.5%	[125]
	Apple	F	480–780	99%	[127]
Escherichia coli	Spinach	R	400–1000	R^2 = 0.97	[20]
Frass	Tomatoes	F	400–800	>99%	[138]
Damaged caused by insects
Fruit fly	Cucumber	T and R	450–1000	82–93%	[58]
Insect infestation	Cherries	T and R	580–1550		[59]
	Jujube	R	900–1700	93.1%	[134]
Callosobruchus maculatus	Mung bean	R	1000–1600	82–85%	[200]
Etiella zinckenella Treitschke	Soybean	T	400–1000	95.6%	[55]

R², coefficient of determination.

^aA, absorbance; F, fluorescence; R, reflectance; T, transmittance.

Table 4. Hyperspectral imaging (HSI) for detecting viable microorganisms and parasites in meat and marine products.

Contaminant	Food	Mode^a	Wavelength (λ, nm)	Accuracy	References
Fecal material and ingesta	Poultry carcasses	R	400–1000	89–98%	[158]
	Poultry carcasses	R	400–900	96.2%	[161]
	Organic residues on equipment surfaces in poultry processing plants	F	500–700	97.5–100%	[168]
Escherichia coli	Pork	S	400–1100	R = 0.88	[142]
Pseudomona	Chicken meat	R	910–1700	R = 0.87	[101]
Enterobacteriaceae	Chicken meat	R	900–1700	R^2 = 0.87	[141]
	Salmon	R	900–1700	R = 0.95	[181]
LAB	Salmon	R	900–1700	R = 0.940, 0.925	[172]
EPC	Salmon	R	900–1700	R = 0.964, 0.953	[180]
TVC	Chicken meat	R	900–1700	R = 0.94	[143]
	Chicken meat	R	400–1000	R^2 = 0.6833	[163]
	Pork	R	400–1100	R^2 = 0.987, 0.942	[149]
	Pork	R	400–1100	R^2 = 0.9236	[150]
	Pork	R	900–1700	R^2 = 0.82	[152]
	Pork	R	400–1100	R = 0.886, 0.863	[154]
	Beef	S	400–1100	R^2 = 0.95	[74]
	Beef	R	405–970	R^2 = 0.98	[79]
	Beef	S	400–1100	R^2 = 0.96	[145]
	Salmon	R	400–1700	R^2 = 0.985	[144]
	Carp	R	400–1000	R^2 = 0.90	[178]
TBC	Salmon	I	400–1000	88%	[170]
Parasites	Cod	I	400–1000	60.3–70.8%	[62]
	Cod	T	400–1000	58%	[190]
	Clam	T	400–1000	85%	[192]

LAB, lactic acid bacteria; EPC, Enterobacteriaceae and Pseudomonas count; TVC, total viable count; TBC, total bacterial count; R, correlation coefficient; R², coefficient of determination.

^aR, reflectance; S, scattering; T, transmittance; I, interactance; F, fluorescence.

Table 5. Multispectral imaging (MI) models developed from hyperspectral imaging (HSI) models by selecting key wavelengths.

	HSI (full wavelengths)		MI (optimized model)
Food sample: contaminant	Wavelength (λ, nm)	Statistical indicators	Selected wavelengths	Statistical indicators	References
Wheat grains: mildew damage	400–1000 nm	Calibration model: R^2C = 0.89, RMSEC = 0.67	Four wavelengths: 450, 561, 861, and 917 nm	Calibration model: R^2C = 0.89, RMSEC = 0.68	[32]
		Validation model: R^2V = 0.87, RMSEV = 0.73		Validation model: R^2V = 0.87, RMSEV = 0.74
Chicken fillets: Pseudomona loads	900–1700 nm	Calibration model: R = 0.88, RMSE = 0.60 log10 CFU/g Cross-validation model: R = 0.83, RMSE = 0.71 log10 CFU/g Prediction model: R = 0.81, RMSE = 0.80 log10 CFU/g	Fourteen wavebands in five spectral segments: 1138–1155, 1195–1198, 1392–1395, 1452–1455, and 1525–1529 nm	Calibration model: R = 0.91, RMSE = 0.55 log10 CFU/g Cross–validation model: R = 0.87, RMSE = 0.65 log10 CFU/g Prediction model: R = 0.88, RMSE = 0.64 log10 CFU/g	[101]
Chicken fillets: Enterobacteriaceae loads	930–1450	Calibration model: R^2 = 0.88, RMSE = 0.35 log10 CFU/g	Three wavelengths: 930, 1121, and 1345 nm	Calibration model: R^2 = 0.89, RMSE = 0.33 log10 CFU/g	[141]
		Cross-validation model: R^2 = 0.82, RMSE = 0.45 log10 CFU/g		Cross–validation model: R^2 = 0.86, RMSE = 0.40 log10 CFU/g
		Prediction model: R^2 = 0.85, RMSE = 0.47 log10 CFU/g		Prediction model: R^2 = 0.87, RMSE = 0.45 log10 CFU/g
Chicken fillets: TVC	910–1700 nm	Calibration model: RC = 0.97, RMSEC = 0.37 log10 CFU/g Cross-validation model: RCV = 0.93, RMSECV = 0.57 log10 CFU/g, RPD = 2.60	Ten wavelengths: 961, 1054, 1081, 1084, 1191, 1198, 1201, 1208, 1218, and 1328 nm	Calibration model: RC = 0.96, RMSEC = 0.42 log10 CFU/g Cross-validation model: RCV = 0.93, RMSECV = 0.54 log10 CFU/g, RPD = 2.75	[143]
Salmon: LAB	900–1700 nm	RP = 0.929, RMSEP = 0.515 log10 CFU/g	Eight wavelengths: 1155, 1255, 1373, 1376, 1436, 1641, 1665, and 1689 nm	RP = 0.925, RMSEP = 0.531 log10 CFU/g	[172]
Salmon flesh: Enterobacteriaceae loads	900–1700 nm	RP = 0.954, RPD = 3.313, RMSEP = 0.481 log10 CFU/g	Nine wavelengths: 931, 1138, 1175, 1242, 1359, 1628, 1641, 1652, and 1655 nm	RP = 0.964, RPD = 3.715, RMSEP = 0.429 log10 CFU/g	[180]
Salmon flesh: Enterobacteriaceae loads	900–1700 nm	RP = 0.94, RPD = 2.97, RMSEP = 0.53 log10 CFU/g	Eight wavelengths: 924, 931, 964, 1068, 1262, 1373, 1628, and 1668 nm	RP = 0.95, RPD = 3.23, RMSEP = 0.47 log10 CFU/g	[181]
Carp: Escherichia coli loads	400–1000 nm	R^2P = 0.88, RMSEP = 0.26 log10 CFU/g, RPD = 5.47	Six wavelengths: 424, 451, 545, 567, 585, and 610 nm	R^2P = 0.87, RMSEP = 0.27 log10 CFU/g, RPD = 5.22	[182]

R, correlation coefficient; R², coefficient of determination and the corresponding root mean square error (RMSE) in: calibration (R_C/R²_C, RMSE_C), cross-validation (R_CV/R²_CV, RMSE_CV), and prediction (R_P/R²_P, RMSE_P); RPD, residual predictive deviation.

Siripatrawan, U.; Makino, Y.; Kawagoe, Y.; Oshita, S. Rapid Detection of Escherichia coli Contamination in Packaged Fresh Spinach using Hyperspectral Imaging. Talanta 2011, 85(1), 276–281.

 PubMed Web of Science ®Google Scholar

Peng, Y.K.; Zhang, J.; Wang, W.; Li, Y.Y.; Wu, J.H.; Huang, H. Potential Prediction of the Microbial Spoilage of Beef using Spatially Resolved Hyperspectral Scattering Profiles. Journal of Food Engineering 2011, 102(2), 163–169.

 Web of Science ®Google Scholar

Peng, Y.K.; Zhang, J.; Wu, J.H.; Huang, H. Hyperspectral Scattering Profiles for Prediction of the Microbial Spoilage of Beef. Proceeding of SPIE 7315, Sensing for Agriculture and Food Quality and Safety 2009, 73150Q–1.

 Google Scholar

Panagou, E.Z.; Papadopoulou, O.; Carstensen, J.M.; Nychas, G.J. Potential of Multispectral Imaging Technology for Rapid and Non-Destructive Determination of the Microbiological Quality of Beef Filets during Aerobic Storage. International Journal of Food Microbiology 2014, 174, 1–11.

 PubMed Web of Science ®Google Scholar

Feng, Y.Z.; Sun, D.W. Determination of Total Viable Count (TVC) in Chicken Breast Fillets by Near-Infrared Hyperspectral Imaging and Spectroscopic Transforms. Talanta 2013a, 105, 244–249.

 PubMed Web of Science ®Google Scholar

Wu, D.; Sun, D.W. Potential of Time Series-Hyperspectral Imaging (TS-HSI) for Non-Invasive Determination of Microbial Spoilage of Salmon Flesh. Talanta 2013c, 111, 39–46

 PubMed Web of Science ®Google Scholar

Sone, I.; Olsen, R.L.; Sivertsen, A.H.; Eilertsen, G.; Heia, K. Classification of Fresh Atlantic Salmon (Salmo salar L.) Fillets Stored under Different Atmospheres by Hyperspectral Imaging. Journal of Food Engineering 2012, 109(3), 482–489.

 Web of Science ®Google Scholar

Cheng, J.H.; Sun, D.W. Rapid and Non-Invasive Detection of Fish Microbial Spoilage by Visible and Near Infrared Hyperspectral Imaging and Multivariate Analysis. LWT - Food Science and Technology 2015a, 62(2), 1060–1068.

 Web of Science ®Google Scholar

Barbin, D.F.; ElMasry, G.; Sun, D.W.; Allen, P.; Morsy, N. Non-Destructive Assessment of Microbial Contamination in Porcine Meat using NIR Hyperspectral Imaging. Innovative Food Science & Emerging Technologies 2013, 17, 180–191.

 Web of Science ®Google Scholar

He, H.J.; Sun, D.W.; Wu, D. Rapid and Real-Time Prediction of Lactic Acid Bacteria (LAB) in Farmed Salmon Flesh using Near-Infrared (NIR) Hyperspectral Imaging Combined with Chemometric Analysis. Food Research International 2014, 62, 476–483.

 Web of Science ®Google Scholar

Feng, Y.Z.; Sun, D.W. Near-infrared Hyperspectral Imaging in Tandem with Partial Least Squares Regression and Genetic Algorithm for Non-Destructive Determination and Visualization of Pseudomonas loads in Chicken Fillets. Talanta 2013b,109, 74–83.

 Google Scholar

Feng, Y.Z.; ElMasry, G.; Sun, D.W.; Walsh, D.; Morcy, N. Near-Infrared Hyperspectral Imaging and Partial Least Squares Regression for Rapid and Reagentless Determination of Enterobacteriaceae on Chicken Fillets. Food Chemistry 2013, 138(2–3), 1829–1836.

 PubMed Web of Science ®Google Scholar

Tao, F.; Peng, Y.; Li, Y.; Chao, K.; Dhakal, S. Simultaneous Determination of Tenderness and Escherichia coli Contamination of Pork using Hyperspectral Scattering Technique. Meat Science 2012, 90(3), 851–857.

 PubMed Web of Science ®Google Scholar

He, H.J.; Sun, D.W. Inspection of Harmful Microbial Contamination Occurred in Edible Salmon Flesh using Imaging Technology. Journal of Food Engineering 2015b, 150, 82–89.

 Web of Science ®Google Scholar

Takeuchi, K.; Frank, J.F. Confocal Microscopy and Microbial Viability Detection for Food Research. Journal of Food Protection 2001, 64(12), 2088–2102.

 PubMed Web of Science ®Google Scholar

Muthomi, J.W.; Ndung’u, J.K.; Gathumbi, J.K.; Mutitu, E.W.; Wagacha, J.M. The Occurrence of Fusarium Species and Mycotoxins in Kenyan Wheat. Crop Protection 2008, 27(8), 1215–1219.

 Web of Science ®Google Scholar

Rammanee, K.; Hongpattarakere, T. Effects of Tropical Citrus Essential Oils on Growth, Aflatoxin Production, and Ultrastructure Alterations of Aspergillus flavus and Aspergillus parasiticus. Food and Bioprocess Technology 2011, 4(6), 1050–1059.

 Web of Science ®Google Scholar

Notermans, S.; Dufrenne, J.; Van Schothorst, M. Enzyme-linked Immunosorbent Assay for Detection of Clostridium botulinum Toxin Type A. Japanese Journal of Medical Science and Biology 1978, 31(1), 81–85.

 PubMedGoogle Scholar

Saunders, G.C.; Bartlett, M.L. Double-Antibody Solid-phase Enzyme Immunoassay for the Detection of Staphylococcal Enterotoxin A. Applied and Environmental Microbiology 1977, 34(5), 518–22.

 PubMed Web of Science ®Google Scholar

Monis, J.; Bestwick, R.K. Serological Detection of Grapevine Associated Closteroviruses in Infected Grapevine Cultivars. Plant Disease 1997, 81(7), 802–808.

 PubMed Web of Science ®Google Scholar

Rowhani, A.; Uyemoto, J.K.; Golino, D.A. A Comparison between Serological and Biological Assays in Detecting Grapevine Leafroll Associated Viruses. Plant Disease 1997, 81(7), 799–801.

 PubMed Web of Science ®Google Scholar

Fæste, C.K.; Plassen, C.; Løvberg, K.E.; Moen, A.; Egaas, E. Detection of Proteins from the Fish Parasite Anisakis simplex in Norwegian Farmed Salmon and Processed Fish Products. Food Analytical Methods 2015, 8(6), 1390–1402.

 Web of Science ®Google Scholar

Werner, M.T.; Fæste, C.K.; Levsen, A.; Egaas, E. A Quantitative Sandwich ELISA for the Detection of Anisakis simplex Protein in Seafood. European Food Research and Technology 2011, 232(1), 157–166.

 Web of Science ®Google Scholar

Nöckler, K.; Reckinger, S.; Broglia, A.; Mayer-Scholl, A.; Bahn, P. Evaluation of a Western Blot and ELISA for the Detection of anti-Trichinella-IgG in Pig Sera. Veterinary Parasitology 2009, 163(4), 341–347.

 PubMed Web of Science ®Google Scholar

Ogunremi, O.; Benjamin, J. Development and Field Evaluation of a New Serological Test for Taenia saginata Cysticercosis. Veterinary Parasitology 2010, 169(1–2), 93–101.

 PubMed Web of Science ®Google Scholar

Ramahefarisoa, R.M.; Rakotondrazaka, M.; Jambou, R.; Carod, J.F. Comparison of ELISA and PCR Assays for the Diagnosis of Porcine Cysticercosis. Veterinary Parasitology 2010, 173(3–4), 336–339.

 Web of Science ®Google Scholar

Goel, M.; Whitmire, E.; Mariakakis, A.; Saponas, T.S.; Joshi, N.; Morris, D.; Guenter, B.; Gavriliu, M.; Borriello, G.; Patel, S.N. HyperCam: Hyperspectral Imaging for Ubiquitous Computing Applications In UbiComp 2015 - Proceedings of the 2015. ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2015, 145–156.

 Google Scholar

Malorny, B.; Paccassoni, E.; Fach, P.; Bunge, C.; Martin, A.; Helmuth, R. Diagnostic real-time PCR for Detection of Salmonella in Food. Applied and Environmental Microbiology 2004, 70(12), 7046–7052.

 PubMed Web of Science ®Google Scholar

Golmohammadi, M.; Cubero, J.; Penalver, J.; Quesada. J.; Lopez, M.; Llop, P. Diagnosis of Xanthomonas Axonopodis pv. citri, Causal Agent of Citrus Canker, in Commercial Fruits by Isolation and PCR-based Methods. Journal of Applied Microbiology 2007, 103(6), 2309–2315.

 PubMed Web of Science ®Google Scholar

Cuttell, L.; Owen, H.; Lew-Tabor, A.E.; Traub, R.J. Bovine Cysticercosis-development of a Real-time PCR to Enhance Classification of Suspect Cysts Identified at Meat Inspection. Veterinary Parasitology 2013, 194(1), 65–69.

 PubMed Web of Science ®Google Scholar

Fernández-Ibáñez, V.; Soldado, A.; Martínez-Fernández, A.; de la Roza-Delgado, B. Application of Near Infrared Spectroscopy for Rapid Detection of Aflatoxin B1 in Maize and Barley as Analytical Quality Assessment. Food Chemistry 2009, 113(2), 629–634.

 Web of Science ®Google Scholar

Yao, H.; Hruska, Z.; Kincaid, R.; Brown, R.; Bhatnagar, D.; Cleveland, T. Detecting Maize Inoculated with Toxigenic and Atoxigenic Fungal Strains with Fluorescence Hyperspectral Imagery. Biosystems Engineering 2013, 115(2), 125–135.

 Web of Science ®Google Scholar

Fæste, C.K.; Moen, A.; Schniedewind, B.; Anonsen, J.H.; Klawitter, J.; Christians, U. Development of Liquid Chromatography-Tandem Mass Spectrometry Methods for the Quantitation of Anisakis simplex Proteins in Fish. Journal of Chromatography A 2016, 1432, 58–72.

 PubMed Web of Science ®Google Scholar

Kandpal, L.M.; Lee, S.; Kim, M.S.; Bae, H.; Cho, B.K. Short Wave Infrared (SWIR) Hyperspectral Imaging Technique for Examination of Aflatoxin B₁ (AFB₁) on Corn Kernels. Food Control 2015, 51, 171–176.

 Google Scholar

Yao, H.; Hruska, Z.; Brown, R.; Cleveland, T. Hyperspectral Bright Greenish-Yellow Fluorescence (BGYF) Imaging of Aflatoxin Contaminated Corn Kernels Proceeding of SPIE.2006, 6381, 63810B-1-63810B-8.

 Google Scholar

Elvira, L.; Durán, C.M.; Urréjola, J.; Montero de Espinosa, F.R. Detection of Microbial Contamination in Fruit Juices using Non-Invasive Ultrasound. Food Control 2014, 40, 145–150.

 Web of Science ®Google Scholar

Hafsteinsson, H.; Parker, K.; Chivers, R.; Rizvi, S. Application of Ultrasonic Waves to Detect Sealworms in Fish Tissue. Journal of Food Science 1989, 54(2), 244–247.

 Web of Science ®Google Scholar

He, H.J.; Sun, D.W. Microbial Evaluation of raw and Processed Food Products by Visible/Infrared, Raman and Fluorescence Spectroscopy. Review. Trends in Food Science and Technology 2015c, 46(2), 199–210.

 Web of Science ®Google Scholar

Kim, S.; Park, J.; Chun, J.H.; Lee, S.M. Determination of Ergosterol used as a Fungal Marker of Red Pepper (Capsicum annuum L.) Powders by Near-Infrared Spectroscopy. Food Science and Biotechnology 2003, 12(3), 257–261.

 Web of Science ®Google Scholar

Hafsteinsson, H.; Rizvi, S. A Review of the Sealworm Problem. Journal of Food Protection 1987, 50(1), 70–84.

 PubMed Web of Science ®Google Scholar

Bublitz, C.G.; Choudhury, G.S. Effect of Light Intensity and Color on Worker Productivity and Parasite Detection Efficiency during Candling of Cod Fillets. Journal of Aquatic Food Product Technology 1992, 1(2), 75–89.

 Google Scholar

Coelho, P.A.; Soto, M.E.; Torres, S.N.; Sbarbaro, D.G.; Pezoa, J.E. Hyperspectral Transmittance Imaging of the Shell-Free Cooked Clam Mulinia edulis for Parasite Detection. Journal of Food Engineering 2013, 117(3), 408–416.

 Web of Science ®Google Scholar

Ausmus, B.S.; Hilty, J.W. Reflectance Studies of Healthy, Maize Dwarf Mosaic Virus-Infected, and Helminthosporium maydis-Infected Corn Leaves. Remote Sensing of Environment 1971, 2, 77–81.

 Google Scholar

Bauriegel, E.; Giebel, A.; Geyer, M.; Schmidt, U.; Herppich, W.B. Early Detection of Fusarium Infection in Wheat using Hyper-Spectral Imaging. Computers and Electronics in Agriculture 2011, 75(2), 304–312.

 Web of Science ®Google Scholar

Yeh, Y.H.; Chung, W.C.; Liao, J.Y.; Chung, C.L.; Kuo, Y.F.; Lin, T.T. Strawberry Foliar Anthracnose Assessment by Hyperspectral Imaging. Computers and Electronics in Agriculture 2016, 122, 1–9.

 Web of Science ®Google Scholar

Qin, J.; Burks, T.; Zhao, X.; Niphadkar, N.; Ritenour, M. Multispectral Detection of Citrus Canker using Hyperspectral Band Selection. Transactions of the ASABE 2011a, 54(6), 2331–2341.

 Web of Science ®Google Scholar

MacDonald, S.L.; Staid, M.; Staid, M.; Cooper, M.L. Remote Hyperspectral Imaging of Grapevine Leafroll-Associated Virus 3 in Cabernet Sauvignon Vineyards. Computers and Electronics in Agriculture 2016, 130, 109–117.

 Web of Science ®Google Scholar

Naidu, R.; Perry, E.; Pierce, F.; Mekuria, T. The Potential of Spectral Reflectance Technique for the Detection of Grapevine leafroll-associated virus-3 in Two Red-Berried Wine Grape Cultivars. Computers and Electronics in Agriculture 2009, 66(1), 38–45.

 Web of Science ®Google Scholar

Lee, H.; Kim, M.S.; Lim, H.S.; Park, E.; Lee, W.H.; Cho, B.K. Detection of Cucumber Green Mottle Mosaic Virus-Infected Watermelon Seeds using a Near-Infrared (NIR) Hyperspectral Imaging System: Application to Seeds of the “Sambok Honey” Cultivar. Biosystems Engineering 2016, 148, 138–147.

 Web of Science ®Google Scholar

Wang, W.; Heitschmidt, G.; Windham, W.; Feldner, P.; Ni, X.; Chu, X. Feasibility of Detecting Aflatoxin B1 on Inoculated Maize Kernels Surface using Vis/NIR Hyperspectral Imaging. Journal of Food Science 2015b, 80(1), M116–M122.

 PubMed Web of Science ®Google Scholar

Pearson, T.C.; Wicklow, D.T.; Maghirang, E.B.; Xie, F.; Dowell, F.E. Detecting Aflatoxin in Single Corn Kernels by Transmittance and Reflectance Spectroscopy. Transactions of the ASAE 2001, 44(5), 1247–1254.

 Google Scholar

Firrao, G.; Torelli, E.; Gobbi, E.; Raranciuc, S.; Bianchi, G.; Locci, R. Prediction of Milled Maize Fumonisin Contamination by Multispectral Image Analysis. Journal of Cereal Science 2010, 52(2), 327–330.

 Web of Science ®Google Scholar

Senthilkumar, T.; Jayas, D.S.; White, N.D.G.; Fields, P.G.; Gräfenhan, T. Detection of Fungal Infection and Ochratoxin A Contamination in Stored Barley using Near-Infrared Hyperspectral Imaging. Biosystems Engineering 2016, 147, 162–173.

 Web of Science ®Google Scholar

Del Fiore, A.; Reverberi, M.; Ricelli, A.; Pinzari, F.; Serranti, S.; Fabbri, A.A.; Bonifazi, G.; Fanelli, C. Early Detection of Toxigenic Fungi on Maize by Hyperspectral Imaging Analysis. International Journal of Food Microbiology 2010, 144(1), 64–71.

 PubMed Web of Science ®Google Scholar

Williams, P.; Geladi, P.; Britz, T.; Manley, M. Investigation of Fungal Development in Maize Kernels using NIR Hyperspectral Imaging and Multivariate Data Analysis. Journal of Cereal Science 2012, 55(3), 272–278.

 Web of Science ®Google Scholar

Zhang, H.; Paliwal, J.; Jayas, D.S.; White, N.D.G. Classification of Fungal Infected Wheat Kernels using Near-Infrared Reflectance Hyperspectral Imaging and Support Vector Machine. Transactions of the ASABE 2007, 50, 1779–1785.

 Google Scholar

Siripatrawan, U.; Makino, Y. Monitoring Fungal Growth on Brown Rice Grains using Rapid and Non-Destructive Hyperspectral Imaging. International Journal of Food Microbiology 2015, 199, 93–100.

 PubMed Web of Science ®Google Scholar

Li, J.; Huang, W.; Tian, X.; Wang, C.; Fan, S.; Zhao, C. Fast Detection and Visualization of Early Decay in Citrus using Vis-NIR Hyperspectral Imaging. Computers and Electronics in Agriculture 2016, 127, 582–592.

 Web of Science ®Google Scholar

Lorente, D.; Blasco, J.; Serrano, A.J.; Soria-Olivas, E.; Aleixos, N.; Gómez-Sanchis, J. Comparison of ROC Feature Selection Method for the Detection of Decay in Citrus Fruit using Hyperspectral Images. Food and Bioprocess Technology 2013, 6(12), 3613–3619.

 Web of Science ®Google Scholar

Zhao, X.; Burks, T.F.; Qin, J.; Ritenour, M.A. Effect of Fruit Harvest Time on Citrus Canker Detection using Hyperspectral Reflectance Imaging. Sensing and Instrumentation for Food Quality and Safety 2010, 4(3), 126–135.

 Google Scholar

Qin, J.; Burks, T.; Zhao, X.; Niphadkar, N.; Ritenour, M. Development of a Two-Band Spectral Imaging System for Real-Time Citrus Canker Detection. Journal of Food Engineering 2012, 108(1), 87–93.

 Web of Science ®Google Scholar

Kim, M.S.; Chen, Y.R.; Cho, B.K.; Chao, K.; Yang, C.C.; Lefcourt, A.M.; Chan, D. Hyperspectral Reflectance and Fluorescence Line-Scan Imaging for Online Defects and Fecal Contamination Inspection of Apples. Sensing and Instrumentation for Food Quality and Safety 2007, 1(3), 151–159.

 Google Scholar

Yang, C.C.; Kim, M.S.; Kang, S.; Cho, B.K.; Chao, K.; Lefcourt, A.M.; Chan, D.E. Red to Far-Red Multispectral Fluorescence Image Fusion for Detection of Fecal Contamination on Apples. Journal of Food Engineering 2012, 108(2), 312–319.

 Web of Science ®Google Scholar

Yang, C.C.; Kim, M.S.; Millner, P.; Chao, K.; Cho, B.K.; Mo, C.; Lee, H.; Chan, D.E. Development of Multispectral Imaging Algorithm for Detection of Frass on Mature Red Tomatoes. Postharvest Biology and Technology 2014, 93, 1–8.

 Web of Science ®Google Scholar

Lu, R.; Ariana, D.P. Detection of Fruit Fly Infestation in Pickling Cucumbers using a Hyperspectral Reflectance/Transmittance Imaging System. Postharvest Biology and Technology 2013, 81, 44–50.

 Web of Science ®Google Scholar

Xing, J.; Guyer, D. Detecting Internal Insect Infestation in Tart Cherry using Transmittance Spectroscopy. Postharvest Biology and Technology 2008, 49(3), 411–416.

 Web of Science ®Google Scholar

Liu, G.; He, J.; Wang, S.; Luo, Y.; Wang, W.; Wu, L.; Si, Z.; He, X. Application of Near-Infrared Hyperspectral Imaging for Detection of External Insect Infestations on Jujube Fruit. International Journal of Food Properties 2016, 19(1), 41–52.

 Web of Science ®Google Scholar

Levin, R.E. PCR detection of Aflatoxin Producing Fungi and its Limitations. Review. International Journal of Food Microbiology 2012, 156(1), 1–6.

 PubMed Web of Science ®Google Scholar

Huang, M.; Wan, X.; Zhang, M.; Zhu, Q. Detection of Insect-Damaged Vegetable Soybeans using Hyperspectral Transmittance Image. Journal of Food Enginnering 2013, 116(1), 45–49.

 Web of Science ®Google Scholar

Yoon, S.C.; Park, B.; Lawrence, K.C.; Windham, W.R.; Heitschmidt, G.W. Line-scan Hyperspectral Imaging System for Real-Time Inspection of Poultry Carcasses with Fecal Material and Ingesta. Computers and Electronics in Agriculture 2011, 79(2), 159–168.

 Web of Science ®Google Scholar

Park, B.; Lawrence, K.C.; Windham, W.R.; Smith, D.P. Performance of Hyperspectral Imaging System for Poultry Surface Fecal Contaminant Detection. Journal of Food Engineering 2006, 75(3), 340–348.

 Web of Science ®Google Scholar

Qin, J.; Chao, K.; Kim, M.S.; Kang, S.; Cho, B.K.; Jun, W. Detection of Organic Residues on Poultry Processing Equipment Surfaces by LED-Induced Fluorescence Imaging. Applied Engineering in Agriculture 2011b, 27(1), 153–161.

 Web of Science ®Google Scholar

He, H.J.; Sun, D.W. Selection of Informative Spectral Wavelength for Evaluating and Visualising Enterobacteriaceae Contamination of Salmon Flesh. Food Analytical Methods 2015d, 8(10), 2427–2436.

 Web of Science ®Google Scholar

Ye, X.; Iino, K.; Zhang, S. Monitoring of Bacterial Contamination on Chicken Meat Surface using a Novel Narrowband Spectral Index Derived From Hyperspectral Imagery Data. Meat Science 2016, 122, 25–31.

 PubMed Web of Science ®Google Scholar

Wang, W.; Peng, Y.K.; Zhang, X.L. Study on Modeling Method of Total Viable Count of Fresh Pork Meat based on Hyperspectral Imaging System. Spectroscopy and Spectral Analysis 2010, 30(2), 411–415.

 Web of Science ®Google Scholar

Wang, W.; Peng, Y.K.; Huang, H.; Wu, J. Application of Hyperspectral Imaging Technique for the Detection of Total Viable Bacteria Count in Pork. Sensor Letters 2011, 9(3), 1024–1030.

 Web of Science ®Google Scholar

Tao, F.; Wang, W.; Li, Y.; Peng, Y.; Wu, J.; Shan, J.; Zhang, L. A Rapid Nondestructive Measurement Method for Assessing the Total Plate Count on Chilled Pork Surface. Spectroscopy and Spectral Analysis 2010, 30(12), 3405–3409.

 Web of Science ®Google Scholar

Tsakanikas, P.; Pavlidis, D.; Panagou, E.; Nychas, G.J. Exploiting Multispectral Imaging for Non-Invasive Contamination Assessment and Mapping of Meat Samples. Talanta 2016, 161, 606–614.

 PubMed Web of Science ®Google Scholar

Sivertsen, A.H.; Heia, K.; Hindberg, K.; Godtliebsen, F. Automatic Nematode Detection in Cod Fillets (Gadus morhua L.) by Hyperspectral Imaging. Journal of Food Engineering 2012, 111(4), 675–681.

 Web of Science ®Google Scholar

Pozio, E. Searching for Trichinella: Not All Pigs are Created Equal. Trends in Parasitology 2014, 30(1), 4–11.

 PubMed Web of Science ®Google Scholar

Gonzalez, M.; Jaramillo, E. The Association between Mulinia edulis (Mollusca, Bivalvia) and Edotea magellanica (Crustacea, Isopoda) in Southern Chile. Revista Chilena de Historia Natural 1991, 64, 37–51.

 Web of Science ®Google Scholar

Shahin, M.A.; Symons, S.J. Design of a Multispectral Imaging System for Detecting Mildew Damage on Wheat Kernels. The Canadian Society for Engineering in Agricultural, Food, Environmental and Biological Systems, 2009, Paper CSBE09-701.

 Google Scholar

Cheng, J.H.; Sun, D.W. Rapid Quantification Analysis and Visualization of Escherichia coli Loads in Grass Carp Fish Flesh by Hyperspectral Imaging Method. Food and Bioprocess Technology 2015c, 8(5), 951–959.

 Web of Science ®Google Scholar