Near-Infrared Spectroscopy and Geostatistical Analysis for Modeling Spatial Distribution of Analytical Constituents in Bulk Animal By-Product Protein Meals

Fearn

et al. 2019

Biosystems Engineering

Self Cite

A NIR spectroscopy-based real-time monitoring system is proposed to sample and analyse agro-industrial raw materials transported in bulk in a single stage, easing and optimising the evaluation process of incoming lots at reception of agri-food plants. NIR analysis allows rapid and cost-effective analytical results to be obtained, and hence to rethink current sampling protocols. For this purpose, multistage and adaptive sampling designs were tested in this paper, which have been reported (in soil science and ecology) to be more flexible and efficient than conventional strategies to study patterns of clustering or patchiness, which can be the result of natural phenomena. The additional spatial information provided by NIR has also been exploited, using geostatistical analysis to model the spatial pattern of key analytical constituents in Processed Animal Proteins (PAPs). This study addresses the assessment of two kinds of quality/safety issues in PAP lots-moisture accumulation and cross-contamination. After a simulation study, qualitative and quantitative analyses were carried out to make a performance comparison between sampling designs. Results show that sampling densities below 10-15% demonstrated higher estimation errors, failing to represent the actual spatial patterns, while a stratified adaptive cluster sampling design achieved the best performance.

Section: Lotsmentioning

confidence: 99%

“…Bearing these constraints in mind, research was recently initiated to explore a new approach for the analysis of raw materials in bulk directly inside the transport unit (trucks, containers, etc. ), before unloading (Adame-Siles et al, 2017). From an analytical standpoint, this approach is based on near-infrared (NIR) spectroscopy.…”

Section: Introductionmentioning

confidence: 99%

Multistage and adaptive sampling protocols combined with near-infrared spectral sensors for automated monitoring of raw materials in bulk

Fearn

et al. 2019

Biosystems Engineering

Self Cite

“…The application of NIRS to this task could resolve some of the constraints of the existing methodologies by performing rapid and cost-effective analysis. On this basis, research was recently initiated to implement a real-time NIRS-based monitoring system for the in-situ characterization of raw materials at delivery points of production plants [18]. The methodology proposed by the authors relies on using NIRS optical probes to sample loads of transport units of products in bulk.…”

Section: Introductionmentioning

confidence: 99%

Performance comparison of sampling designs for quality and safety control of raw materials in bulk: A simulation study based on NIR spectral data and geostatistical analysis

Chemometrics and Intelligent Laboratory Systems

Fearn

et al. 2020

This study exploits the potential of near infrared (NIR) spectroscopy to deliver a measurement for each sampling point. Furthermore, it provides a protocol for the modelling of the spatial pattern of analytical constituents. On the basis of these two aspects, the methodology proposed in this work offers an opportunity to provide a real-time monitoring system to evaluate raw materials, easing and optimising the existing procedures for sampling and analysing products transported in bulk. In this paper, Processed Animal Proteins (PAPs) were selected as case study, and two types of quality/safety issues were tested in PAP lots -induced by moisture and cross-contamination. A simulation study, based on geostatistical analysis and the use of a set of sampling protocols, made a qualitative analysis possible to compare the representation of the spatial surfaces produced by each design. Moreover, the Root Mean Square Error of Prediction (RMSEP), calculated from the differences between the analytical values and the geostatistical predictions at unsampled locations, was used to measure the performance in each case. Results show the high sensitivity of the process to the sampling plan used -understood as the sampling design plus the sampling intensity. In general, a gradual decrease in the performance can be observed as the sampling intensity decreases, so that unlike for higher intensities, the too low ones resulted in oversmoothed surfaces which did not manage to represent the actual distribution. Overall, Stratified and Simple Random samplings achieved the best results in most cases. This indicated that an optimal balance between the design and the intensity of the sampling plan is imperative to perform this methodology.

“…6,7 The possibility of performing on-site qualitative and quantitative analysis has also supported new methodologies combining geospatial and NIR analysis, with which quality attributes are mapped in order to evaluate their spatial distribution in bulk lots of raw materials. 8 Consequently, the potential of a new evaluation model based on performing sampling and analysis at the same time is of great interest. This would lead to the opportunity of achieving cost-effective and fit-for-purpose protocols with which real-time decisions could be made.…”

Section: Introductionmentioning

confidence: 99%

Assessing the potential of two customized fiber-optic probes for on-site analysis of bulk feed grains

Pérez‐Marín

Proceedings of the 18th International Conference on Near Infrared Spectroscopy

et al. 2019

Feed grains are typically transported in bulk and a statistically representative sample of the grain in the truckload is usually required to be taken to the laboratory for wet chemistry or at-line near infrared (NIR) spectroscopy analysis. Currently, most methodologies make use of a physical sampling probe, which mechanically or pneumatically withdraws samples from various depths. Nevertheless, not only is the implementation of this approach expensive and time-consuming, but it is also limited by low sample throughput. In this context, the authors' group is involved in a large research and development project to find more efficient and cost-effective ways of sampling and analyzing bulk raw materials at the reception level. This work presents a piece of this research focused on the evaluation of the optical performance of two fiber-optic probes designed for automated use as immersion probes in truckloads. It is worth noting the rather different optical design of these two diffuse reflectance probes. Probe A features eight bundles (37 fibers/bundle), four for measurement and four for illumination, 0.5 m in length, and four sapphire windows located around the probe diameter. Probe B has one fiber-optic bundle for measurement (7 fibers) and one for illumination (19 fibers), 3 m in length, and a stainless-steel head with two sapphire windows. The experimental design of this laboratory study aimed at imitating the control of bulk lots of two sort of cereals (maize and wheat). For this purpose, a sample of each cereal was placed into a container (0.34 m in width, 0.4 m in length and 0.25 in height) for analysis. To avoid interferences caused by design, both probes were attached to the same Fourier transform-NIR instrument (Matrix-F, Bruker Optics), and spectra were acquired in the range 834.2-2502.4 nm using the same settings. Two different strategies for recording reference spectra were followed in each case (before the first scan and either after every measurement or after every set of 10 measurements). Noisy regions and spectral repeatability were assessed as a first step towards the evaluation of the feasibility of these probes for performing on-site analysis.