Milk is among the foods most often named in records of economically motivated adulteration (Moore et al., 2012). The commonest fraud is the oldest: water is added to increase volume, and other substances are then added to hide the dilution by restoring density, apparent protein, sweetness or keeping quality. Reviews list urea, starch, sucrose, salt, whey, vegetable oil, detergents, neutralisers and preservatives such as formalin and hydrogen peroxide among the materials found (Poonia et al., 2017). The melamine contamination of infant formula in China in 2008 showed how serious the consequences can be when a nitrogen-rich compound is used to defeat a protein test based on total nitrogen, and the burden of milk fraud falls most heavily where control systems are weakest (Handford et al., 2016).

Reference methods for each adulterant exist, but they are slow, need a laboratory and test for one substance at a time. Near-infrared spectroscopy offers a different bargain: a measurement that takes seconds, needs no reagents and responds to the overall composition of the sample. Miniaturised instruments have brought that capability to the point of collection (Crocombe, 2018). Feasibility studies have shown that infrared spectra, combined with multivariate analysis, can distinguish adulterated from authentic milk under laboratory conditions (Kasemsumran et al., 2007; Santos et al., 2013).

The gap between such demonstrations and a dependable screening tool is methodological. This article proposes a procedure for closing it. No experiment is reported: the procedure is assembled from established chemometric practice and has yet to be validated on milk by the route described.

Near-infrared radiation, conventionally 780 to 2500 nm, is absorbed by overtones and combinations of the fundamental vibrations of C-H, N-H and O-H bonds. The bands are broad and overlapping, and in milk they are dominated by water, whose strong absorptions near 1450 and 1940 nm leave relatively narrow windows in which fat, protein and lactose can be seen. Milk also scatters light strongly, because fat globules and casein micelles are comparable in size to the wavelengths used, so a spectrum carries physical as well as chemical information. International guidance on near-infrared analysis of milk products sets out the consequences for sampling, calibration and validation (ISO and IDF, 2006).

Portable instruments add constraints of their own. Many cover only part of the range, often the short-wave region up to about 1700 nm, with lower resolution and signal-to-noise ratio than bench spectrometers, and units of the same model differ from one another (Crocombe, 2018). None of this prevents useful work, but it means that an adulterant present at a low concentration contributes a small signal on top of large natural variation, and that only a carefully built multivariate model can separate the two.

The procedure is arranged as a cycle of six stages, shown in Figure 1, because a calibration is never finished: monitoring in use feeds back into the sample set and the model. The stages are described below.

The effort involved is front-loaded. Collecting authentic milk across seasons takes a year, and the developer should resist releasing a model built on a single season's samples. A dairy company, a cooperative union or a regulator is better placed than a single collection centre to carry that cost, and a shared calibration maintained centrally and distributed to many devices is the natural arrangement. Operators need training in sample handling far more than in chemometrics, since most field errors arise from temperature, mixing and dirty optics.

Results must be interpreted within a decision rule fixed in advance. A positive screen is grounds for holding a lot and testing it by a reference method; it is not proof of fraud and should not, by itself, lead to a penalty. A negative screen means only that the sample resembles authentic milk within the sensitivity of the method.

The procedure is a proposal and has not been tested here. What a portable instrument can detect in liquid milk by following it is an open, empirical question that depends on the adulterant, the device and the variability of the local milk. Near-infrared spectroscopy is a bulk technique. Dilution with water and additions that change composition by an appreciable fraction are plausible targets; contaminants that are harmful at trace levels, of which melamine is the best-known example, are not realistic targets for direct screening of liquid milk with this technology, and claims to the contrary should be examined against the validation design that supports them.

Further matters would need to be established before routine use: how many authentic samples are needed for a one-class model to be stable across seasons, how often models require updating, how well transfer methods cope with the unit-to-unit differences of low-cost devices, and whether the rate of false alarms is low enough for collection staff to keep trusting the instrument. Each can be answered by a study that follows the stages set out above and publishes its independent test results in full.

Portable near-infrared spectrometers put a rapid, reagent-free measurement in the hands of those who receive milk, but the measurement becomes a screening test only through calibration. The procedure proposed in this article treats that calibration as a managed life cycle: a clearly stated question, a reference set that captures natural variation, disciplined acquisition, restrained pre-processing, a one-class model backed by targeted regressions, validation on independent milk, and continuing control after deployment. Followed carefully, it should yield models whose limits are known, which is the property a screening method most needs.

To be declared by the author(s) before publication.

To be declared by the author(s) before publication.