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Enregistrement W3036326974 · doi:10.1353/vcr.2019.0048

Milk and the Victorians: The Problem of Adulteration

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

Notice bibliographique

RevueVictorian review · 2019
Typearticle
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueCulinary Culture and Tourism
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésConsumption (sociology)SustenanceMilk productsHistoryAgricultural economicsFood scienceLawChemistryPolitical scienceEconomicsSociologySocial science

Résumé

récupéré en direct d'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...

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,910
Score d'incertitude au seuil0,343

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,007
Tête enseignante GPT0,200
Écart entre enseignants0,193 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2019
Routes d'admission1
Résumé présentoui

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