Rapid and Non-Destructive Monitoring of Moisture Content in Livestock Feed Using a Global Hyperspectral Model.

Byoung-Kwan Cho,Santosh Lohumi,Daniel Dooyum Uyeh,Tusan Park,Won Suk Lee,Seungmin Woo,Juntae Kim,Yushin Ha

doi:10.3390/ani11051299

Abstract

Simple SummaryMoisture content is an important parameter for monitoring the quality of feed and feed materials as its established ranges serve as markers for safe storage, mixing, and feeding animals. The moisture content of feed materials changes very rapidly and necessitates rapid measurement. Current moisture content measurement methods are time-consuming, destructive, and require specialized skills. This often causes reduced and/or delayed testing, which results in the spoilage of feed and feed materials. Additionally, the improper balance of dry matter intake which is inversely proportional to moisture content often causes metabolic diseases for animals consuming the diet. To solve these, we have developed a rapid and non-destructive global hyperspectral model that could quantify moisture content in feed materials. Our results show that the developed model is robust, could provide a method to measure the distribution of moisture in feed, and has potential for implementation in a commercial setting.The dry matter (DM) content of feed is vital in cattle nutrition and is inversely correlated with moisture content. The established ranges of moisture content serve as a marker for factors such as safe storage limit and DM intake. Rapid changes in moisture content necessitate rapid measurements. A rapid and non-destructive global model for the measurement of moisture content in total mixed ration feed and feed materials was developed. To achieve this, we varied and measured the moisture content in the feed and feed materials using standard methods and captured their images using a hyperspectral imaging (HSI) system in the spectral range of 1000–2500 nm. The spectral data from the samples were extracted and preprocessed using seven techniques and were used to develop a global model using partial least squares regression (PLSR) analysis. The range preprocessing technique had the best prediction accuracy (R2 = 0.98) and standard error of prediction (2.59%). Furthermore, the visual assessment of distribution in moisture content made possible by the generated PLSR-based moisture content mapped images could facilitate precise formulation. These applications of HSI, when used in commercial feed production, could help prevent feed spoilage and resultant health complications as well as underperformance of the animals from improper DM intake.

Highlights

Moisture content is an important and widely used indicator in the processing and testing of foods
The small peak at 1530 nm is associated with the N-H stretch first overtone and 1660 nm to the aromatic C-H stretch first overtone [28]. These represent the variation in the moisture, carbohydrate, and protein contents of the samples [28,29]
We demonstrated the possibilities of using hyperspectral imaging technology for the rapid and non-destructive scans of all feed samples passing through the conveyor belt for safe production, storage, and feeding of livestock

Summary

Introduction

Moisture content is an important and widely used indicator in the processing and testing of foods. Since the dry matter in food is inversely related to the moisture it contains, the moisture content is of direct economic importance to the processor and the consumer. Moisture content of feed materials, and total mixed ration (TMR). In some countries such as the Republic of Korea, TMR feed is manufactured in factories, bagged, stored, and sold to farms [2]. This is because most of the raw materials for feed production are sourced internationally, which makes it difficult for small farmers (less than 50 cattle) [3]

Methods

Results

Conclusion