[1]Yao-Ze Feng*, Hai-Tao Zhao, Gui-Feng Jia, Chijioke Ojukwu, and He-Qun Tan, 'Establishment of Validated Models for Non-Invasive Prediction of Rectal Temperature of Sows Using Infrared Thermography and Chemometrics', International journal of biometeorology, 63 (2019), 1405-15 （IF=2.377）
[2]Hai-Tao Zhao, Yao-Ze Feng*, Wei Chen, Gui-Feng Jia,Application of invasive weed optimization and least square support vector machine for prediction of beef adulteration with spoiled beef based on visible near-infrared (Vis-NIR) hyperspectral imaging,Meat Science, 151,2019, 75-81, （IF=3.486）
[3]Yao-Ze Feng*, Wei Yu, Wei Chen, Kuan-Kuan Peng, Gui-Feng Jia,Invasive weed optimization for optimizing one-agar-for-all classification of bacterial colonies based on hyperspectral imaging,Sensors and Actuators B: Chemical,269, 2018, 264-270 （IF=6.393）
[4]Ke-Xin Mu, Yao-Ze Feng*, Wei Chen, Wei Yu, Near infrared spectroscopy for classification of bacterial pathogen strains based on spectral transforms and machine learning,Chemometrics and Intelligent Laboratory Systems, 179,2018, 46-53（IF=2.786）
[5]Chen, W., Yao-Ze Feng*, Jia, G. et al. Application of Artificial Fish Swarm Algorithm for Synchronous Selection of Wavelengths and Spectral Pretreatment Methods in Spectrometric Analysis of Beef Adulteration. Food Anal. Methods 11, 2229–2236 (2018) （IF=2.413）
[6]Yao-Ze Feng, Gerard Downey, Da-Wen Sun, Des Walsh and Jun-Li Xu. Towards improvement in classification of Escherichia coli, Listeria innocua and their strains in isolated systems based on chemometric analysis of visible and near-infrared spectroscopic data. Journal of Food Engineering, 2015, 149:87-96 (SCI，IF=3.625)
[7]Yao-Ze Feng, Gamal ElMasry, Da-Wen Sun, Amalia Scannell, Des Walsh and Noha Morcy. 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 (SCI，IF=5.399, ESI高被引文章)
[8]Yao-Ze Feng and Da-Wen Sun. Determination of total viable count (TVC) in chicken breast fillets by near-infrared hyperspectral imaging and spectroscopic transforms, Talanta, 2013, 105: 244-249 (SCI，IF=4.916)
[9]Yao-Ze Feng and Da-Wen Sun. 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, 2013, 109: 74-83 (SCI，IF=4.916)
[10]Yao-Ze Feng and Da-Wen Sun. Application of hyperspectral imaging in food safety inspection and control: a review. Critical reviews in food science and nutrition, 2012, 52(11): 1039-1058 (SCI，IF=6.704, ESI高被引文章)