On the estimation of the total of vegetable membrane substances

Paloheimo, L.; Paloheimo, I.

doi:10.23986/afsci.71262

Cited by 7 publications

(7 citation statements)

References 1 publication

(2 reference statements)

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“…Walker & Hepburn 1955, Griffith & Jones 1963, the »lignocellulose» of Jarrige (1960), the »acid detergent fiber» of van Soest (1963b) and the »membrane substances» of Paloheimo and Paloheimo (1949). In the present investigation these methods were not applied, since judging by the author's previous findings (Salo 1965 a), at the acid concentration used the first three methods result in a slightly higher figure than the sum of cellulose and lignin presented in Table 2, because in the treatment these two substances and a small part of the hemicellulose remain undissolved.…”

Section: Resultsmentioning

confidence: 99%

On the carbohydrate composition and nutritive value of some cereals

Salo

Kotilainen

1970

AFSci

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The contents and the distribution between the different grain parts of starch, hemicellulose, cellulose, crude lignin, crude protein and ash have been determined for oat, barley, rye and two wheat species. The relative proportions of the different structural units of the hemicellulose have also been determined. Furthermore, a method is proposed for the determination of the cell-wall complex in cereals. Oat and barley differ from wheat and rye to an appreciable extent only in respect of the hull. The hulls consist of strawlike material rich in lignin, cellulose and xylan; they are richer in xylan than the straw proper. The fruit and seed coat enveloping the seed (viz. the bran) contains relatively small amounts of lignin and cellulose. The bran is chiefly composed of hemicellulose with xylose and arabinose as the main components. The main compound of the endosperm is starch, although some hemicellulose and protein are present, whereas only minor amounts of cellulose and lignin can be found. In the samples investigated only small differences were noted between the compositions of wheat, rye, dehulled oat and dehulled barley; wheat had the highest starch content, rye the highest hemicellulose content, and oat the highest contents of crude protein and lignin. The cell-wall complex determined as the residue after enzymatic hydrolysis of the starch agreed well with that obtained by fractionation. On the other hand, the detergent-fibre values differed from the fractionation results considerably, and the conventional crude fibre analysis yielded results which were about 1.5 times that of cellulose. The feed-values of the different grain parts are discussed on the basis of the analysis results obtained.

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Section: Resultsmentioning

confidence: 99%

On the carbohydrate composition and nutritive value of some cereals

Salo

Kotilainen

1970

AFSci

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“…For the supply side, the CNCPS model was heavily based on Peter J. Van Soest’s ideas about the fractionation of carbohydrate (Van Soest, 1967) and protein (Van Soest et al, 1981), which themselves rest on many concepts of the classification of carbohydrate and protein for ruminants dating back to the 1950s with the work of Lauri and Irja Paloheimo (Paloheimo and Paloheimo, 1949).…”

Section: Extant Mathematical Models In Ruminant Productionmentioning

confidence: 99%

ASN-ASAS SYMPOSIUM: FUTURE OF DATA ANALYTICS IN NUTRITION: Mathematical modeling in ruminant nutrition: approaches and paradigms, extant models, and thoughts for upcoming predictive analytics1,2

Tedeschi

2019

Journal of Animal Science

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This paper outlines typical terminology for modeling and highlights key historical and forthcoming aspects of mathematical modeling. Mathematical models ( MM ) are mental conceptualizations, enclosed in a virtual domain, whose purpose is to translate real-life situations into mathematical formulations to describe existing patterns or forecast future behaviors in real-life situations. The appropriateness of the virtual representation of real-life situations through MM depends on the modeler’s ability to synthesize essential concepts and associate their interrelationships with measured data. The development of MM paralleled the evolution of digital computing. The scientific community has only slightly accepted and used MM, in part because scientists are trained in experimental research and not systems thinking. The scientific advancements in ruminant production have been tangible but incipient because we are still learning how to connect experimental research data and concepts through MM, a process that is still obscure to many scientists. Our inability to ask the right questions and to define the boundaries of our problem when developing models might have limited the breadth and depth of MM in agriculture. Artificial intelligence ( AI ) has been developed in tandem with the need to analyze big data using high-performance computing. However, the emergence of AI, a computational technology that is data-intensive and requires less systems thinking of how things are interrelated, may further reduce the interest in mechanistic, conceptual MM. Artificial intelligence might provide, however, a paradigm shift in MM, including nutrition modeling, by creating novel opportunities to understand the underlying mechanisms when integrating large amounts of quantifiable data. Associating AI with mechanistic models may eventually lead to the development of hybrid mechanistic machine-learning modeling. Modelers must learn how to integrate powerful data-driven tools and knowledge-driven approaches into functional models that are sustainable and resilient. The successful future of MM might rely on the development of redesigned models that can integrate existing technological advancements in data analytics to take advantage of accumulated scientific knowledge. However, the next evolution may require the creation of novel technologies for data gathering and analyses and the rethinking of innovative MM concepts rather than spending resources in collecting futile data or amending old technologies.

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“…The primary problems associated with NFE and CF fractions (Van Soest 1994) were realised by Paloheimo (1953), who initiated research to develop improved analytical methods for plant cell wall. In the pioneering work, Paloheimo and co-workers (Paloheimo andPaloheimo 1949, Paloheimo andVainio 1965) used a weak hydrochloric acid and a two-stage ethanol extraction to remove cellular contents to describe vegetable fibre. Despite the correct criticism against fractionating feed carbohydrates into CF and NFE, these methods were too laborious, not applicable to faecal samples and the fibre residue was contaminated with protein.…”

Section: Silage Composition With Reference To Nutrient Availabilitymentioning

confidence: 99%

An overview of silage research in Finland: from ensiling innovation to advances in dairy cow feeding

Huhtanen¹,

Jaakkola²,

Nousiainen³

2013

AFSci

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Because of the climatic conditions, the Finnish milk production research has focused to improve the utilisation of grassland, mainly as conserved forages. The main research areas have been ensiling, evaluation of the forage feeding value, predicting nutrient supply from grass silage-based diet and the effects of forage quality and concentrate supplementation on milk production responses. Due to changes in ensiling technologies and variety of forage crops new silage additives have been adopted. A centralized system for the analysis of forage energy value is based on NIRS calibration. It was calibrated against in vitro pepsin-cellulase solubility method that was validated against in vivo digestibility. The concentration of indigestible neutral detergent fibre was found to be a useful parameter both in empirical models predicting forage digestibility and mechanistic rumen models predicting the amounts of absorbed nutrients. Models predicting relative intake potential of forages and total diet were developed, and an intake model combining animal and diet effects independently of each other was developed. Using meta-analysis approaches a nutrient response model was developed for dairy cows for milk, energy corrected milk and protein yield. Feed evaluation, intake and nutrient response models form now the basis of practical Finnish ration formulation system that can optimize diets according to maximum income over feed cost in addition to minimum feed cost.

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On the estimation of the total of vegetable membrane substances

Cited by 7 publications

References 1 publication

On the carbohydrate composition and nutritive value of some cereals

On the carbohydrate composition and nutritive value of some cereals

ASN-ASAS SYMPOSIUM: FUTURE OF DATA ANALYTICS IN NUTRITION: Mathematical modeling in ruminant nutrition: approaches and paradigms, extant models, and thoughts for upcoming predictive analytics1,2

An overview of silage research in Finland: from ensiling innovation to advances in dairy cow feeding

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