Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro, Italy; Department of Veterinary Science, University of Parma, Via del Taglio 10, 43126 Parma, Italy.
Department of Veterinary Science, University of Parma, Via del Taglio 10, 43126 Parma, Italy.
J Dairy Sci. 2019 Nov;102(11):9622-9638. doi: 10.3168/jds.2019-16770. Epub 2019 Aug 30.
Near-infrared spectroscopy (NIRS) has been widely used to determine various composition traits of many dairy products in the industry. In the last few years, near-infrared (NIR) instruments have become more and more accessible, and now, portable devices can be easily used in the field, allowing the direct measurement of important quality traits. However, the comparison of the predictive performances of different NIR instruments is not simple, and the literature is lacking. These instruments may use different wavelength intervals and calibration procedures, making it difficult to establish whether differences are due to the spectral interval, the chemometric approach, or the instrument's technology. Hence, the aims of this study were (1) to evaluate the prediction accuracy of chemical contents (5 traits), pH, texture (2 traits), and color (5 traits) of 37 categories of cheese; (2) to compare 3 instruments [2 benchtop, working in reflectance (R) and transmittance (T) mode (NIRS-R and NIRS-T, respectively) and 1 portable device (VisNIRS-R)], using their entire spectral ranges (1100-2498, 850-1048, and 350-1830 nm, respectively, for NIRS-R, NIRS-T and VisNIRS-R); (3) to examine different wavelength intervals of the spectrum within instrument, comparing also the common intervals among the 3 instruments; and (4) to determine the presence of bias in predicted traits for specific cheese categories. A Bayesian approach was used to develop 8 calibration models for each of 13 traits. This study confirmed that NIR spectroscopy can be used to predict the chemical composition of a large number of different cheeses, whereas pH and texture traits were poorly predicted. Color showed variable predictability, according to the trait considered, the instrument used, and, within instrument, according to the wavelength intervals. The predictive performance of the VisNIRS-R portable device was generally better than the 2 laboratory NIRS instruments, whether with the entire spectrum or selected intervals. The VisNIRS-R was found suitable for analyzing chemical composition in real time, without the need for sample uptake and processing. Our results also indicated that instrument technology is much more important than the NIR spectral range for accurate prediction equations, but the visible range is useful when predicting color traits, other than lightness. Specifically for certain categories (i.e., caprine, moldy, and fresh cheeses), dedicated calibrations seem to be needed to obtain unbiased and more accurate results.
近红外光谱（NIRS）已广泛用于确定工业中许多乳制品的各种成分特性。在过去的几年中，近红外（NIR）仪器变得越来越容易获得，现在便携式设备可以轻松地在现场使用，从而可以直接测量重要的质量特性。但是，不同 NIR 仪器的预测性能比较并不简单，而且文献也缺乏。这些仪器可能使用不同的波长间隔和校准程序，因此很难确定差异是由于光谱间隔，化学计量方法还是仪器技术引起的。因此，本研究的目的是：（1）评估 37 类奶酪的化学含量（5 个特征），pH 值，质地（2 个特征）和颜色（5 个特征）的预测准确性；（2）使用其整个光谱范围（NIRS-R 和 NIRS-T 分别为 1100-2498nm 和 850-1048nm，而 VisNIRS-R 为 350-1830nm）比较 3 种仪器[2 种台式机，工作在反射（R）和透射（T）模式下（NIRS-R 和 NIRS-T）和 1 种便携式设备（VisNIRS-R）]；（3）检查仪器内光谱的不同波长间隔，并比较 3 种仪器的公共间隔；（4）确定特定奶酪类别的预测特征是否存在偏差。使用贝叶斯方法为每个 13 个特征分别开发了 8 个校准模型。本研究证实，NIR 光谱可用于预测大量不同奶酪的化学成分，而 pH 值和质地特征则预测不佳。颜色的可预测性根据所考虑的特征，所使用的仪器以及仪器内的波长间隔而变化。便携式 VisNIRS-R 仪器的预测性能通常优于 2 种实验室 NIRS 仪器，无论是使用整个光谱还是选择间隔。VisNIRS-R 被发现适合实时分析化学成分，而无需采样和处理。我们的结果还表明，对于准确的预测方程，仪器技术比近红外光谱范围重要得多，但是当预测除亮度以外的颜色特征时，可见光范围是有用的。特别是对于某些类别（例如山羊，发霉和新鲜奶酪），似乎需要进行专用校准以获得无偏且更准确的结果。
