Prediction of the CEC to clay ratio using mid-infrared spectroscopy

The ratio of cation exchange capacity to clay (CCR) has been used as an index of clay mineralogy in subsoils low in organic matter in place of the standard X-ray diffraction measurement. Laboratory determination of this ratio is time-consuming and expensive and involves two analyses. In this paper, the CCR has been successfully predicted from mid-infrared diffuse reflectance spectra using partial least-squares regression (PLSR) with a square-root transformation of the CCR values (R2 = 0.860; root mean squared error of prediction = 0.089; relative per cent deviation = 2.660 for an independent validation set). The most important wavelengths used in the PLSR calibration were identified. The prediction of CCR using mid-infrared spectroscopy provides a cheaper and faster alternative to laboratory determination.

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Rapid tannin profiling of tree fodders using untargeted mid-infrared spectroscopy and partial least squares regression

Plant Methods ◽

10.1186/s13007-021-00715-8 ◽

2021 ◽

Vol 17 (1) ◽

Author(s):

Jordi Ortuño ◽

Sokratis Stergiadis ◽

Anastasios Koidis ◽

Jo Smith ◽

Chris Humphrey ◽

...

Keyword(s):

Infrared Spectroscopy ◽

Least Squares ◽

Partial Least Squares ◽

Partial Least Squares Regression ◽

Multivariate Calibration ◽

Plant Ecology ◽

Coefficient Of Determination ◽

Least Squares Regression ◽

Mid Infrared ◽

Mid Infrared Spectroscopy

Abstract Background The presence of condensed tannins (CT) in tree fodders entails a series of productive, health and ecological benefits for ruminant nutrition. Current wet analytical methods employed for full CT characterisation are time and resource-consuming, thus limiting its applicability for silvopastoral systems. The development of quick, safe and robust analytical techniques to monitor CT’s full profile is crucial to suitably understand CT variability and biological activity, which would help to develop efficient evidence-based decision-making to maximise CT-derived benefits. The present study investigates the suitability of Fourier-transformed mid-infrared spectroscopy (MIR: 4000–550 cm−1) combined with multivariate analysis to determine CT concentration and structure (mean degree of polymerization—mDP, procyanidins:prodelphidins ratio—PC:PD and cis:trans ratio) in oak, field maple and goat willow foliage, using HCl:Butanol:Acetone:Iron (HBAI) and thiolysis-HPLC as reference methods. Results The MIR spectra obtained were explored firstly using Principal Component Analysis, whereas multivariate calibration models were developed based on partial least-squares regression. MIR showed an excellent prediction capacity for the determination of PC:PD [coefficient of determination for prediction (R2P) = 0.96; ratio of prediction to deviation (RPD) = 5.26, range error ratio (RER) = 14.1] and cis:trans ratio (R2P = 0.95; RPD = 4.24; RER = 13.3); modest for CT quantification (HBAI: R2P = 0.92; RPD = 3.71; RER = 13.1; Thiolysis: R2P = 0.88; RPD = 2.80; RER = 11.5); and weak for mDP (R2P = 0.66; RPD = 1.86; RER = 7.16). Conclusions MIR combined with chemometrics allowed to characterize the full CT profile of tree foliage rapidly, which would help to assess better plant ecology variability and to improve the nutritional management of ruminant livestock.

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Investigation on the effectiveness of mid-infrared spectroscopy to predict detailed mineral composition of bulk milk

Journal of Dairy Research ◽

10.1017/s0022029917000826 ◽

2018 ◽

Vol 85 (1) ◽

pp. 83-86 ◽

Cited By ~ 4

Author(s):

Massimo Malacarne ◽

Giulio Visentin ◽

Andrea Summer ◽

Martino Cassandro ◽

Mauro Penasa ◽

...

Keyword(s):

Infrared Spectroscopy ◽

Mineral Composition ◽

Prediction Models ◽

External Validation ◽

Bovine Milk ◽

Least Squares Regression ◽

Mid Infrared ◽

Mid Infrared Spectroscopy ◽

Bulk Milk ◽

Mineral Components

This Research Communication investigated the potential of mid-infrared spectroscopy to predict detailed mineral composition of bovine milk. A total of 153 bulk milk samples were analysed for contents of Ca, Cl, Cu, Fe, K, Mg, Na, P and Zn. Also, soluble and colloidal fractions of Ca, Mg and P were quantified. For each milk sample the mid-infrared spectrum was captured and stored. Prediction models were developed using partial least squares regression and the accuracy of prediction was evaluated using both cross- and external validation. The proportion of variance explained by the prediction models in cross-validation ranged from 34% (Na) to 77% (total P), and it ranged from 13% (soluble Mg) to 54% (Cl−) in external validation. The ratio of the standard deviation of each trait to the standard error of prediction in external validation, which is an indicator of the practical utility of the prediction model, was low and never greater than 2. Results from the current study supported the limited usefulness of mid-infrared spectroscopy to predict minerals present in low concentration in bulk milk. For major mineral components, results from the present research did not match previous findings demonstrating the need for further studies using larger reference datasets.

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