Near-infrared spatially-resolved spectroscopy for milk quality analysis

被引:5
作者
Diaz-Olivares, Jose A. [1 ]
Gote, Martin J. [1 ]
Saeys, Wouter [2 ]
Adriaens, Ines [1 ]
Aernouts, Ben [1 ]
机构
[1] Katholieke Univ Leuven, Dept Biosyst, Div Anim & Human Hlth Engn, Campus Geel,Kleinhoefstr 4, B-2440 Geel, Belgium
[2] Katholieke Univ Leuven, Dept Biosyst, Div Mechatron Biostat & Sensors, Kasteelpk Arenberg 30, B-3001 Leuven, Belgium
关键词
SRS; Spatially resolved spectroscopy; Near-infrared spectroscopy; Food quality control; Milk; BULK OPTICAL-PROPERTIES; DIFFUSE-REFLECTANCE; FOOD QUALITY; RAW-MILK; TECHNOLOGY; PROTEIN; DESIGN; SYSTEM; REGION; FAT;
D O I
10.1016/j.compag.2024.108783
中图分类号
S [农业科学];
学科分类号
09 ;
摘要
To support in -line quality control of raw milk and ensure the close monitoring of the metabolic and udder health of dairy cows, we propose a fiber -optic spatially-resolved spectroscopy (SRS) setup. This setup allows to vary the interaction of long -wave near-infrared (LW-NIR) light with the milk by submerging a separate optical illumination and detection fiber into the sample and altering their relative distance to optimize measurements for specific milk component(s). We evaluated this approach for predicting milk fat, protein, and lactose content and determined the optimal illumination-to-detection distances for each milk component. The region between 1.1 and 1.8 mm was optimal for lactose, and between 2.2 and 3.8 mm for fat and protein. These distance ranges resulted in a root-mean-square error of prediction (RMSEP) of less than 0.10 % weight/weight (wt/wt) for milk fat, and lower than 0.13 % (wt/wt) for protein and lactose. Integration of these distances into a fiber -optic SRS reflectance probe would allow to simultaneously determine the fat, protein, and lactose content of raw milk inline with high accuracy.
引用
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页数:11
相关论文
共 60 条
[41]  
Paiva C L., 2013, Food Industry, DOI DOI 10.5772/53162
[42]   Milk homogenization monitoring: Fat globule size estimation from scattering spectra of milk [J].
Postelmans, Annelies ;
Aernouts, Ben ;
Jordens, Jeroen ;
Van Gerven, Tom ;
Saeys, Wouter .
INNOVATIVE FOOD SCIENCE & EMERGING TECHNOLOGIES, 2020, 60
[43]  
R Core Team, 2022, R: a language and environment for statistical computing
[44]   Robotic milking: Technology, farm design, and effects on work flow [J].
Rodenburg, Jack .
JOURNAL OF DAIRY SCIENCE, 2017, 100 (09) :7729-7738
[45]   Increasing robustness against changes in the interferent structure by incorporating prior information in the augmented classical least-squares framework [J].
Saeys, Wouter ;
Beullens, Katrien ;
Lammertyn, Jeroen ;
Ramon, Herman ;
Naes, Tormod .
ANALYTICAL CHEMISTRY, 2008, 80 (13) :4951-4959
[46]   System design for non-destructive near infrared analyses of chemical components and total aerobic bacteria count of raw milk [J].
Saranwong, Sirinnapa ;
Kawano, Sumio .
JOURNAL OF NEAR INFRARED SPECTROSCOPY, 2008, 16 (04) :389-398
[47]   Spatially Resolved Spectral Powder Analysis: Experiments and Modeling [J].
Scheibelhofer, Otto ;
Wahl, Patrick R. ;
Larcheveque, Boris ;
Chauchard, Fabien ;
Khinast, Johannes G. .
APPLIED SPECTROSCOPY, 2018, 72 (04) :521-534
[48]   Spectral distortions in near-infrared spectroscopy of turbid materials [J].
Schmitt, JM ;
Kumar, G .
APPLIED SPECTROSCOPY, 1996, 50 (08) :1066-1073
[49]  
Shenk J. S., 2007, HDB NEAR INFRARED AN, P347, DOI [10.1201/9781420007374-24, DOI 10.1201/9781420007374-24, DOI 10.1201/9781420007374]
[50]   VALIDATION OF REGRESSION-MODELS - METHODS AND EXAMPLES [J].
SNEE, RD .
TECHNOMETRICS, 1977, 19 (04) :415-428