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Record W3036326974 · doi:10.1353/vcr.2019.0048

Milk and the Victorians: The Problem of Adulteration

2019· article· en· W3036326974 on OpenAlexvenueno aff
Chris Otter

Bibliographic record

VenueVictorian review · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsnot available
Fundersnot available
KeywordsConsumption (sociology)SustenanceMilk productsHistoryAgricultural economicsFood scienceLawChemistryPolitical scienceEconomicsSociologySocial science

Abstract

fetched live from OpenAlex

Milk and the Victorians: The Problem of Adulteration Chris Otter (bio) “Milk may be regarded as a model food, and as a complete food,” declared the analytical chemist George Wigner in 1884. “It is a model food because it is nature’s own food, designed for the sustenance of the young of animals, and, as such, it contains and furnishes all the nutritive properties in due proportion required by a growing animal” (3). British consumption levels of this “model food” were rising in the Victorian period, even if they lagged behind much of northern Europe and the United States. One 1902 estimate suggested that average milk consumption in London had doubled between 1884 and 1901, reaching two-fifths of a pint daily (Swan 89). Milk was, however, the most problematic of foodstuffs. While outlandish claims about the adulteration of milk, particularly the persistent myth about the nefarious addition of sheep’s brains, were almost certainly “utter fiction,” the addition of water and the removal of cream diluted milk’s nutritional quality and potentially exposed consumers to dangerous waterborne pathogens such as typhoid (Morton 72). William Savage, Medical Officer of Health for Somerset, concluded that milk was “fatally easy to adulterate,” and his choice of adverb was deliberate (80). The problem’s scale was first exposed by Arthur Hassall, in several Lancet exposés published between 1851 and 1854. In 1880, Londoners were estimated to be paying seventy to eighty thousand pounds annually for water sold as milk (“London Milk”). One 1892 estimate suggested that “between twenty and thirty thousand additional cows would be required if pure milk only were sold in London” (“Tricks of the London Milk Trade”). Adulteration, then, was both an economic and a public health problem. But how could chemists prove that a particular sample of milk was adulterated? Milk is a composite mixture of proteins, fats, sugars, minerals, vitamins, and trace elements coexisting in various states; it was perhaps “the most delicate and complex fluid in nature,” and was “not to be read like an open book, clearly printed, even by those who have been taught the alphabet” (Sheldon 97). The proportion of fat varied with the cow’s age, time after calving, diet, climate, and milking regimen (Hassall 390). It did not emerge from the cow in anything like a “standard” condition. Drawing a firm boundary between naturally weak and adulterated milk was “very difficult, if not absolutely impossible” (Long 43). Some scoffed at the idea that “normal milk” could be defined: “one might as well talk of a ‘normal potato,’ or a ‘normal cabbage,’ or a ‘normal pig’ ” (Voelcker 250). Chemists set out to find ways to calculate the ratio of fat to other solids. Since fats are lighter than the rest of milk, removing them caused milk’s specific gravity to rise. Water, however, is also lighter than milk, so adding it caused milk’s specific gravity to fall. This allowed for the estimation of either the amount of abstracted cream or of added water through the use [End Page 192] of rudimentary specific-gravity measuring implements: lactometers and creamometers. However, many analysts argued that such instruments were useless because they failed to distinguish between abstracted cream and added water. Moreover, cream could be removed and water added, producing a normal reading. Lactometry declined in popularity after 1880, and laboratory techniques that avoided specific-gravity measurements became more common (Atkins 65). Perhaps the most successful of these was the Babcock test (fig. 1): milk’s non-fatty solids were dissolved with sulphuric acid, and the fat was then separated in a centrifuge (Farrington and Woll 25). It was accurate, fast, and easy, although handling sulphuric acid required care (Farrington and Woll 6–7). Other new forms of testing included techniques of acidity measurement, refractive indices, and bacteriological analysis. The “normal” material composition of milk, below which adulteration could generally be assumed, was established by the 1890s. Paul Veith, the Aylesbury Dairy Company’s chief analyst, analyzed 120,540 samples over eleven years, concluding that normal milk contained 12.9% total solids, of which 4.1% was fat (85). Click for larger view View full resolution Fig. 1. “Milk and Cream Tester’s...

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.390
Threshold uncertainty score0.775

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0140.023
Scholarly communication0.0070.009
Open science0.0020.008
Research integrity0.0070.017
Insufficient payload (model declined to judge)0.0090.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.200
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2019
Admission routes1
Has abstractyes

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