Cats at the Panelis palatability research facility.
The wet petfood market is increasingly oriented to consumer
macro trends, such as product convenience and the humanization
of pets. The increase of chunks in jelly launches, especially
in single servings, echoes this orientation. In 2007, 15% of
wet cat food launches were chunks in jelly vs. 11% in 2006
(Gnpd database, 2008).
Nowadays not only pet tastes and nutritional requirements
must be considered, but the consumer perception and enjoyment
with pets are becoming more important in the petfood industry.
So, research and development teams have to consider more
sophisticated methods for designing products.
Various research fields can be integrated in the product
development process, including physiochemical measurements,
sensory analysis and consumer sciences. These data provide a
multidimensional description of a product. In the petfood
business, the palatability test classically used to evaluate
product performance is based on consumption weighing that
produces a one-dimensional response. Thus, innovative data
treatment methods are needed to relate animals' preferences to
More sophisticated tools
Several statistical methods, such as preference mapping
techniques, allow working out the correlation between
consumers' ratings and product characteristics. In palatability
assessment, the difficulty is posed by the imbalance between
input data structure (product parameters) and output data
The PLS regression (partial least square) is a statistical
method that produces a graphical display of the analyzed
products, with their characteristics, and a mapping of the
consumers' preferences. This method builds a causal scheme of
relationships between sensory data, physicochemical bloc and
hedonic judgments. These analytical scopes are centered on the
assessed products and provide valuable descriptions of the
consumers' preference profile. As opposed to linear regression
analysis, PLS offers the possibility to model one variable
(cat's food intake for instance) by a very high number of
explanatory variables without risking an over adjustment.
This method was used in a comprehensive study carried out by
SPF's Wet R&D team to investigate the interactions between
texture of wet cat food and its palatability on chunks in jelly
Meat by-products are the main components of wet products,
but the variability in raw materials leads to a high
variability of texture. On the other hand, another material
commonly used in wet products has a high textural regularity:
the jelly. Rheological properties of gels after retorting can
be predicted with relatively good precision, which makes them
especially suitable models for research.
Four jellies were made using four different gelling agents
with thermo-reversible properties to build an extended textural
field (Figure 1). One jelly was representative of common
jelly-based products found on the market. The same dose of
palatability enhancer (SPF C'Sens) was added to each of the
Instrumental texture analysis was done using a penetrometry
technique (measuring the penetrability of a material) for
measurement of gel hardness, rigidity and springiness. The
texture measurement showed highly significant divergences in
texture amongst the four prototypes. The jellies were then
assessed by a human sensory panel for texture and odor
The jellies' characterization (texture, sensory
description), and "cats first choices" were processed as input
data. A full design and specific palatability testing in a cat
expert panel (Panelis, France) provided cumulated individual
consumption values as output data. The PLS permitted the
comparison of individual preferences of the 40 cats with
product characteristics to show the relationship between
texture differences and palatability.
Texture and palatability correlated
Data obtained for the four jelly products (texture profiles,
sensory evaluation and palatability preferences) are displayed
in Figure 2. In this PLS plot, jellies A and D (harder gels)
are divergent from jellies B and C (more elastic gels). The
human sensory description of texture is correlated to the
instrumentally measured texture according to the positioning of
these variables. Cats' preferences are described by their
relative proximity to product points.
The plot highlights a sensorial consensus centered on soft
and elastic jellies that are characterized by higher odor
intensity and first choices values. Hard and rigid jellies were
clearly rejected by the majority of cats. This apparent
negative correlation between palatability and gel strength lead
to the hypothesis of a relationship with the flavor
Matrix effect involved?
., 2006) explained that the more cohesive the structure (with a
highly organized polysaccharide network), the more the volatile
aromatic compounds are retained, and vice versa. His work
indicates the matrix effect that can explain, in this study,
how texture has a retention effect on flavor-release in the
jellies and thus influence cats' preferences.
The sensory perception of gel texture can be divided into
three main areas:
Further experiments were conducted on gel texture modulation
to produce controlled variations in jelly textures, and to
assess cats' perceptions toward those differences. Odor induced
consumption was shown in cats with another experimental device
that showed the importance of flavor release in palatability.
These observations corroborate the evidence of the matrix
effect in cat preferences.
Complex and multi-variable
From the product design point of view, formulation and
process parameters are known to impact directly on gel texture.
Thus, through the matrix effect, they impact indirectly on
palatability. For this reason, it is necessary to consider gel
texture as a way to improve the performance of palatability
The addition of chunks, the mineral content, pH properties,
moisture transfers and thermal treatments affect gel texture.
Based on these investigations, SPF's Wet Research team built a
model of physicochemical interactions in chunks in jelly. The
study of feline palatability remains a complex and
multi-variable area. Improving knowledge requires developing
innovative, sensitive data processing techniques.
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