The quality of predicted plant‐, soil‐, and animal‐response values from near‐infrared (NIR) reflectance spectra depends on the ability to generate appropriate NIR models. The first step in the development of NIR models is collection of spectral data. Limited work, however, has been reported that compares NIR models for prediction of forage nutritive value when the spectra are obtained from devices with different spectral ranges and resolutions. The objectives of this study were to (a) develop and evaluate NIR spectroscopy models using a benchtop‐type (FOSS) and two handheld NIR devices (microPHAZIR and DLP NIRscan Nano EVM) to predict crude protein (CP), acid detergent fiber (ADF), amylase and sodium sulfite‐treated neutral detergent fiber (aNDF), and in vitro true dry matter digestibility (IVTD) of dried ground forage grass samples and (b) compare predictions among the three NIR devices. Switchgrass (Panicum virgatum L.) and bermudagrass [Cynodon dactylon (L.) Pers] hay samples were scanned with the NIR devices and analyzed with wet chemistry for development of NIR prediction models. Among devices, the r² of validation values for aNDF models ranged from .81 to .87; all other r² values were >.86 and as high as .98 with standard error of prediction (SEP; g kg⁻¹) ranging from 8.1 to 11.5 for CP, 19.1 to 23.8 for aNDF, 14.2 to 20.0 for ADF, and 26.8 to 49.9 for IVTD. The FOSS benchtop NIR prediction models consistently had the highest r² and lowest SEP values; however, the predictive power for both handheld devices was similar to the benchtop‐type device.

中文翻译：

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比较台式和手持式近红外光谱仪以确定饲料的营养价值

根据近红外（NIR）反射光谱预测的植物，土壤和动物响应值的质量取决于生成适当NIR模型的能力。开发NIR模型的第一步是收集光谱数据。然而，已经报道了有限的工作，当从具有不同光谱范围和分辨率的设备获得光谱时，可以比较NIR模型来预测饲料的营养价值。这项研究的目的是（a）使用台式（FOSS）和两个手持式NIR设备（microPHAZIR和DLP NIRscan Nano EVM）开发和评估NIR光谱模型，以预测粗蛋白（CP），酸性洗涤剂纤维（ADF） ），淀粉酶和亚硫酸钠处理的中性洗涤剂纤维（aNDF），干燥的牧草样本的体外真实干物质消化率（IVTD），（b）比较三种NIR设备之间的预测。柳枝（（Panicum virgatum L.）和百慕大草（Cynodon dactylon（L.）Pers）干草样品用NIR设备扫描，并用湿化学法分析以开发NIR预测模型。在设备中，aNDF模型的验证值的r ²为0.81至0.87；所有其他r ²值均> .86并高达.98，标准预测误差（SEP; g kg ^-1）对于CP为8.1至11.5，对于aNDF为19.1至23.8，对于ADF为14.2至20.0，以及26.8 IVTD为49.9。FOSS台式NIR预测模型始终具有最高的r ²和最低的SEP值。但是，这两种手持设备的预测能力都与台式设备相似。