A Framework for Representative Sampling for NIR Analysis – Theory of Sampling (TOS)

Esbensen, Kim H.; Romañach, Rodolfo J.

doi:10.1201/b22513-23

Cited by 2 publications

(33 citation statements)

References 1 publication

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“…This oftenoverlooked sampling duality is treated in more detail in. [2][3][4][5][6][7] The universal objective of proper sampling is how to counteract adverse influences from material heterogeneity on the primary sampling (or sensor signal acquisition), subsampling, and sample preparation/presentation processes. The Theory and Practice of Sampling is the only comprehensive theoretical and practical framework with which to eliminate or reduce maximally, the adverse Total Sampling Error (TSE) effects on Measurement Uncertainty (MU total ).…”

Section: The Minimum Tos Responsibility For Nir Analystsmentioning

confidence: 99%

“…A much overlooked duality: governing principles (GP), potential sampling errors (SE) and Sampling Unit Operations (SUO) are identical for physical sampling (left) and for sensor signal acquisition, "PAT sampling" (right). The TOS framework is intended to be applied identically to both stationary lots and for moving lots, see [2][3][4][5][6][7] for full treatment. Illustration copyright by KHE Consulting, reproduced with permission.…”

Section: The Replication Experiments (Re)mentioning

confidence: 99%

“…There is really never any excuse (logistically, economically) not to do this. Full descriptions can be found in, [3][4][5][6] while the following brief suffices for the present review purpose.…”

Section: The Replication Experiments (Re)mentioning

confidence: 99%

“…Composite sampling is always better than grab sampling when it comes to counteracting heterogeneity's influence on sampling variability. [2][3][4][5][6] At the primary sampling stage, there very nearly always does not exist any other options than these two principal sampling alternatives. Indeed this holds for both stationary sampling as well as for process sampling; there is an overwhelming practical experience and published literature evidence backing up these statements, [1][2][3][4][5][6] (and numerous references therein).…”

Section: The Replication Experiments (Re)mentioning

confidence: 99%

“…1 Part 1 forms an indispensable basis for Part 2 and should be consulted also for its detailed description of the terms used in the Theory and Practice of Sampling (TOS), especially regards sampling errors and ways and means of how to avoid, or minimise, their influence on the total Measurement Uncertainty. [1][2][3][4][5][6][7] Still, for the stalwart NIR practitioner, Part 2 will lead directly to the answer to the question: "What does all this TOS mean specifically for NIR analysis?" by answering a set of typical concerns from the analytical NIR realm.…”

Section: Introductionmentioning

confidence: 99%

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Before reliable near infrared spectroscopic analysis - the critical sampling proviso. Part 2: Particular requirements for near infrared spectroscopy

Esbensen

Abu-Khalaf

2022

Journal of Near Infrared Spectroscopy

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Non-representative sampling of materials, lots and processes intended for NIR analysis is often fraught with hidden contributions to the full Measurement Uncertainty MUtotal = TSE + TAENIR. The Total Sampling Error (TSE) can dominate over the Total Analytical Error TAENIR by factors 5-10-25, depending on the degree of material heterogeneity and the specific sampling procedures employed to produce the minuscule aliquot, which is the only material actually analysed. Part 1 presented a brief of all sampling uncertainty elements in the “lot-to-aliquot” pathway, which must be identified and correctly managed (eliminated or reduced maximally), especially the sampling bias, as a prerequisite to achieve fully representative sampling. The key for this is the Theory of Sampling (TOS), which is presented in two parts in a novel compact fashion. Part 2 introduces (i) application of TOS to process sampling, specifically addressing and illustrating how this manifests itself in the realm of PAT, Process Analytical Technology, and (ii) an empirical safeguard facility, termed the Replication Experiment (RE), with which to estimate the effective sampling-plus-analysis uncertainty level (MUtotal) associated with NIR analysis. The RE is a defence against compromising the analytical responsibilities. Ignorance, either caused by lack of awareness or training, or by wilful neglect, of the demand for TSE minimisation, is a breach of due diligence concerning analysis QC/QA. Part 2 ends with a special focus on: “What does all this TOS mean specifically for NIR analysis?”. The answer to this question will perhaps surprise many. There is nothing special that need worrying NIR analysts relative to professionals from all other analytical modalities; all that is needed is embedded in the general TOS framework. Still, this review concludes by answering a set of typical concerns from NIR practitioners.

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Section: The Minimum Tos Responsibility For Nir Analystsmentioning

confidence: 99%

Section: The Replication Experiments (Re)mentioning

confidence: 99%

Section: The Replication Experiments (Re)mentioning

confidence: 99%

Section: The Replication Experiments (Re)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Before reliable near infrared spectroscopic analysis - the critical sampling proviso. Part 2: Particular requirements for near infrared spectroscopy

Esbensen

Abu-Khalaf

2022

Journal of Near Infrared Spectroscopy

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show abstract

Before reliable near infrared spectroscopic analysis - the critical sampling proviso. Part 1: Generalised theory of sampling

Esbensen

Abu-Khalaf

2022

Journal of Near Infrared Spectroscopy

View full text Add to dashboard Cite

Non-representative sampling of materials, lots and processes intended for near infrared (NIR) analysis is often contributing hidden additions to the full Measurement Uncertainty (MUtotal = TSE + TAENIR). The Total Sampling Error (TSE) can dominate over the Total Analytical Error (TAENIR) by factors ranging from 5 to 10 to even 25 times, depending on material heterogeneity and the specific sampling procedures employed to produce the minuscule aliquot, which is the only material analysed. This review (Parts 1 and 2), extensively referenced with easily available complementing literature, presents a brief of all sampling uncertainty elements in the “lot-to-aliquot” pathway, which must be identified and correctly managed (eliminated or maximally reduced) in order to achieve, and to be able to document, fully minimised MUtotal. The more irregular and pervasive the heterogeneity, the higher the number of increments needed to reach ‘fit-for-purpose representativity’. A particular focus is necessary regarding the sampling bias, which is fundamentally different from the well-known analytical bias. Whereas the latter can easily be subjected to bias correction, the sampling bias is non-correctable by any posteori means, notably not by chemometrics, nor statistics. Instead, all sampling operations must be designed to exclude the so-called Incorrect Sampling Errors (ISE), which are the hidden bias-generating agents. The key element in this endeavour is representative sampling and sub-sampling before analysis, as laid out by the Theory of Sampling (TOS), which is presented here in a novel compact fashion along with a complement of selected examples and demonstrations. TOS includes a safeguard facility, termed the Replication Experiment (RE), which enables estimation of the total sampling- plus-analysis uncertainty level (MUtotal) associated with NIR analysis (the RE is, for practical and logistical reasons, found in Part 2). Neglecting the TSE effects from the before-analysis domain is lack of due diligence. TOS to the fore!

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A Framework for Representative Sampling for NIR Analysis – Theory of Sampling (TOS)

Cited by 2 publications

References 1 publication

Before reliable near infrared spectroscopic analysis - the critical sampling proviso. Part 2: Particular requirements for near infrared spectroscopy

Before reliable near infrared spectroscopic analysis - the critical sampling proviso. Part 2: Particular requirements for near infrared spectroscopy

Before reliable near infrared spectroscopic analysis - the critical sampling proviso. Part 1: Generalised theory of sampling

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