# Visible-NIR spectroscopic authentication assay for the classification of lamb meat according to pasture-finishing duration

Rey-Cadilhac Lª, Andueza Dª, and Prache Sª

ªUniversité Clermont Auvergne, INRAE, VetAgro Sup, UMR1213 Herbivores, 63122 St-Genès-Champanelle, France

Corresponding author: Sophie Prache – Email: sophie.prache@inrae.fr

## Abstract

Spectral analysis of meat combined with chemometric analysis has been identified as a promising tool for authenticating livestock-animal diets. The objectives of this study were (i) to determine whether the visible-NIR spectrum of perirenal adipose tissue (PAT) and caudal adipose tissue (CAT) can reliably discriminate lambs pasture-finished for different durations before slaughter, and (ii) to analyze the kinetics of appearance and stabilization of the visible-NIR spectrum-based pasture signature in PAT and CAT. Four groups of 50–55 lambs were used over three years: lambs finished on lucerne pasture for 0 (L0, concentrate-fed in stall), 21 (L21), 42 (L42) and 63 (L63) days before slaughter. Partial least squares discriminant analysis was applied on PAT or CAT visible-NIR spectra to discriminate the groups. No one adipose tissue reliably discriminated the four groups, with less than 62% lambs correctly classified. However, visible-NIR spectroscopy was able to discriminate stall-fed (L0) from pasture-finished (L21+L42+L63) lambs, with an accuracy of 93.8% and 87.5% lambs correctly classified based on PAT and CAT spectra, respectively. The lucerne pasture fingerprint (or signature) on visible-NIR spectrum appeared between 0 and 42 days in more than 95% of lambs. It stabilized between 42 and 63 days in CAT, but had not stabilized within the range of grazing durations pre-slaughter explored in PAT. Further research into shorter and longer pasture-finishing durations could help determine more precisely the time required for the pasture signature to appear and stabilize in animal tissues.

**Keywords:** grazing, signature, chemometrics, discriminant analysis, alfalfa, fat
# 1. Introduction

Spectral analysis on animal-source products combined with chemometric analysis has been identified as a promising tool for the authentication of livestock-animal diets (Prache & Thériez, 1999; Monahan et al., 2018; Prache et al., 2020; Logan et al., 2021; Rey-Cadilhac et al., 2024). Spectral analysis can focus on different regions of the light absorption spectrum, e.g. the visible, near-infrared, or mid-infrared range. Discriminant analysis on the visible spectrum of lamb adipose tissues has been used to distinguish lambs fed grain in-stall from pasture-finished lambs, and the absorption regions of carotenoids and haem pigments have been identified as key regions of the visible spectrum for this discrimination (Dian et al., 2007; Huang et al., 2015, Rey-Cadilhac et al., 2024). However, analysis of the visible spectrum in adipose tissue failed to reliably discriminate lambs according to pasture-finishing duration before slaughter (Rey-Cadilhac et al., 2024). As spectroscopy in the visible-near-infrared (Vis-NIR) region captures more information than visible spectroscopy, it has been shown to improve the reliability of authentication of lamb dietary history compared to visible spectroscopy (Dian et al., 2008; Huang et al., 2015). Dian et al. (2008) showed better discrimination between stall-fed lambs and pasture-fed lambs using the Vis-NIR spectrum than the visible spectrum of adipose tissue (97.7% vs 94.9% lambs correctly classified, respectively). Likewise, Huang et al. (2015) showed better discrimination between stall-fed lambs vs pasture-fed lambs vs lambs concentrate-finished indoors for 28 days after pasture-feeding using the Vis-NIR spectrum than the visible spectrum of adipose tissue (99.0% vs 95.1% lambs correctly classified, respectively).

Furthermore, Rey-Cadilhac et al. (2024) showed that the pasture signature (i.e., fingerprint of pasture-feeding on the spectrum) appeared between 0 and 21 days in the visible spectrum of adipose tissue, but was not completely stabilized within the range of pasture-finishing duration explored (0-63 days). However, the time of appearance and stabilization of the pasture signature in the Vis-NIR spectrum of adipose tissue has not yet been studied.

Here we investigated the change in adipose tissue Vis-NIR absorbance spectrum in lambs switched from an in-stall concentrate-based diet to outdoor lucerne grazing for various durations before slaughter. This study had two objectives: (i) to determine whether the Vis-NIR spectrum of adipose tissues can discriminate lambs according to pasture-finishing duration before slaughter, and (ii) to analyze the kinetics of appearance and stabilization of the pasture signature in the Vis-NIR spectrum of adipose tissues.
## 2. Material and methods

The trial was carried out at the Herbipôle experimental station in France (INRAE, UE1414, doi: https://doi.org/10.15454/1.5572318050509348E12). The French Ministry of Higher Education, Research and Innovation ethics committee approved all the procedures (committee approval 14289-2017042411419479V4).

### 2.1. Animals, diets and slaughter procedures

The trial compared four groups of lambs finished for different lengths of time on lucerne pasture before slaughter, i.e. 0 day (L0), 21 days (L21), 42 days (L42) and 63 days (L63), as described in Rey-Cadilhac et al. (2024). The trial was repeated for three successive years. Nineteen male Romane lambs were used in each treatment group each year, giving a total of 228 lambs used over the three years of the trial. Each year, all lambs born in the experimental farm during a 4-week interval were used, i.e. between 8th and 30th April in year 1, between 6th and 30th April in year 2, and between 28th March and 30th April in year 3. The dates of turn-out to pasture were planned to accommodate the slaughter schedule, so that the lambs were slaughtered when they were about five months old. Carcasses of commercially acceptable weight and fatness are obtained at this age of slaughter for male Romane lambs fed lucerne pasture after weaning (Devincenzi et al., 2019; Rivaroli et al., 2019, Eiras et al., 2022). The lambs were randomized to the four treatment groups on the basis of live weight (LW) at birth, and growth rate between birth and the turning out to pasture of the first group of pasture-finished lambs (i.e., at an average age of 3 months). The mean LW at birth was 4.0 ± 0.8 kg (SD), and mean age at slaughter was 143 ± 13 days.

Prior to being taken out to pasture, L21, L42 and L63 lambs were fed a concentrate-based diet with *ad libitum* straw. At pasture, L21, L42 and L63 lambs grazed together on the same plots in order to control the effects of pasture characteristics. Two dry ewes were also added to train them to graze. At turn-out to pasture, the lambs underwent a 5-day dietary transition period. During this period, the lambs grazed on a grass plot and were allowed access to an adjacent lucerne plot for increasing lengths of time over the five days. After this 5-day transition period, lambs joined the other lambs already grazing lucerne. Rotation between lucerne plots was managed to allow lambs *ad libitum* access to the green lucerne leaves. After each grazing period, the plots were topped and the cuttings removed to guarantee good-
quality lucerne regrowth. Lambs were medicated for digestive nematodes and *Moniezia* if necessary (on the basis of monthly faecal egg counts).

Indoors, lambs were also kept in a single group and fed concentrate and straw. To avoid potential confounding effects between the age and LW of lambs at slaughter and the duration of finishing on lucerne pasture, the amount of concentrate fed was adjusted to achieve a similar growth rate between stall-fed lambs and those lucerne pasture-finished. Long troughs and racks allowed the lambs to access the feed at the same time. At a fixed time each morning, any feed refusals were removed and weighed before the daily concentrate was distributed. Lambs in all treatments had access to water and salt blocks *ad libitum*.

Lambs were slaughtered at the INRAE experimental slaughterhouse by stunning followed by exsanguination. Evisceration and skinning were carried out according to commercial practice, and the carcasses were then stored 24 h in a refrigerated chiller at 4 °C.

## 2.2. Measurements

### 2.2.1. Animal performances and carcass characteristics

The weight of the lambs was measured at birth, weekly (at a fixed time each day), on the day they were taken out to pasture, and at slaughter. The carcasses were weighed at 24 h *postmortem* before being graded with a fatness score from 1 (very low) to 15 (very high).

### 2.2.2. Visible-near-infrared absorbance spectrum of the perirenal and caudal adipose tissues (400 to 2500 nm)

A sample of perirenal adipose tissue (PAT) and a sample of caudal adipose tissue (CAT) were collected from lamb carcasses at 24 h *postmortem*, packed in aluminum foil, placed in a plastic bag under vacuum, and stored at – 20 °C. Then, within 3 months *postmortem*, adipose tissues were thawed at room temperature for ~ 2 h, unpacked, then blood vessels and connective tissue were removed, and the adipose tissues were ground to 2–3 mm particle size using a Moulinex type 320 chopper. The absorbance spectrum of PAT and CAT was measured twice (two replicates), at 2-nm intervals across the range of wavelengths from 400 nm to 2500 nm, using a FOSS-NIRSystem 6500 scanning monochromator (FOSS-NIRSystems, Silver Spring, MD). For each adipose tissue, the absorbance
spectrum was then averaged from the two replicates. Note that 10 lambs were too lean to collect a
sample of PAT for absorbance spectrum measurement. Note too that year-1 CAT sampling started late
after slaughtering began, so only 157 CAT samples were analyzed in total (42, 38, 39 and 38 samples
from L0, L21, L42 and L63 lambs, respectively).

**2.3. Statistical analysis**

The R software version 4.2.2 (R Foundation for Statistical Computing, Vienna, Austria) was used for the
statistical analyses except where explicitly stated otherwise. Note that all the pasture-fed lambs were
run together (in the same pasture plot except during the 5-day transition shift), and all the concentrate-
fed lambs were run together (in the same pen indoors), thus avoiding confounding the treatment effect
with the pen or pasture plot effect and allowing the animal to be considered the statistical unit (Seo et
al., 2019).

2.3.1. *Animal performances and carcass characteristics*

The effect of the treatment on animal performances and carcass characteristics was analyzed by ANOVA with *treatment* as a fixed effect and *year* as a random effect, followed by the Tukey test for pairwise comparisons where appropriate (package *lme4*). Data for PAT weight were log-transformed to comply with the assumptions of normality and homoscedasticity (assessed by residual analyses using graphical plots). As this transformation did not allow these assumptions to be respected for carcass fatness, this variable was analyzed using a non-parametric equivalent test (adjusted rank transform test; Leys & Schumann, 2010; Wobbrock et al., 2011; package *ARTool*).

**2.2.2. Partial least squares-discriminant analysis**

Discrimination of the treatment groups using the absorbance spectrum of each adipose tissue was
performed using the partial least squares-discriminant analysis (PLS-DA) of the *caret* package. The
analyses were carried out both on raw and pre-processed spectra. Preprocessing treatments are used
in NIRS-related studies to extract relevant spectral information that can be influenced by features such
as temperature, water content, particle size, light scattering, sample handling, etc. There is no standard
procedure for determining which pretreatment or combination of pretreatments will give the best results
in the development of NIRS models. Delwiche and Reeves (2004) considered that the selection of
pretreatments should be based on the judgment of the model developer. In this regard, experience with
lamb fat (Dian et al., 2008; Guy et al., 2011; Huang et al., 2015) indicated that the use of the combination
of a scattering correction with a standard normal variate transformation followed by a detrend correction
(SNV-D; Barnes et al., 1989) and a derivative mathematical pretreatment corresponding to the first
derivative (1stD) was appropriate for the development of NIRS models on this product (lamb fat).
Furthermore, in qualitative applications, Shenk and Westerhaus (1994) suggested that the use of raw
spectra may be beneficial for obtaining optimal results for discriminating between groups. With these
considerations, the models were developed on lamb fat spectra taking into account the combination of
pretreatments mentioned above, or without the use of pretreatments. The spectra underwent pre-
processing methods with the WinISI III software version 1.63 (Infrasoft International, Port Matilda, PA).
Normalization gives equal weight to the absorbance of each wavelength, detrend corrects the baseline,
and the first derivative highlights peaks. Figures 1 and 2 show the raw and pre-processed PAT and CAT
spectra, respectively, averaged by group. In addition, principal component analysis of pre-processed
PAT and CAT spectra are presented in Figures S1 and S2. Only the PLS-DA models providing the best
accuracy are presented in the Results section (full results obtained for both the pre-processed and the
raw spectra are presented in Table S1). It should be noted that the best accuracy was obtained with raw
or pre-processed spectra depending on the tissue and the groups discriminated. Prior to this analysis,
the outliers were identified for each adipose tissue and treatment group using the Mahalanobis distance
(H) between each spectrum and the mean spectrum from the principal component analysis (H > 3,
Garrido-Varo et al., 2019). One raw CAT spectrum and three pre-processed PAT spectra were identified
as outliers and consequently removed.

For both adipose tissues, different PLS-DA were applied to discriminate the treatment groups based on
the absorbance spectrum. For each PLS-DA, the database was first randomly divided into a training set
(70% of the data) and a test set (30% of the data), balancing the distribution of groups and years in the
training and test sets. The first step was to optimize the model by selecting the optimal number of latent
variables. This step was carried out on the training set with series of PLS-DA models with 1 to 10 latent
variables, using the caret package's train function (with 4-fold PLS-DA cross-validation repeated 10
times). The optimal number of latent variables was selected as the one giving the lowest cross-validated
classification error. Then, the final PLS-DA model with the optimal number of latent variables was
validated on the test set through performance indicators: overall accuracy, class error rate, sensitivity,
specificity, false negative rate (FNR) and false positive rate (FPR), as described in Rey-Cadilhac et al. (2024). The regions in the spectrum the most important for the discrimination between the groups were identified when a variable importance in projection (VIP) score was equal to or over 1 (Chong and Jun, 2005). In addition, a permutation test with 100 permutations was applied after each PLS-DA as described in Rey-Cadilhac et al. (2024) and give a *p*-value assessing that the results are significantly different from a discrimination where the lambs are randomly assigned to the treatment-groups (Szymańska et al., 2012).

This statistical procedure was applied to discriminate (i) the four treatment groups, (ii) the stall-fed lambs (L0 lambs) from lucerne pasture-finished lambs (L21+L42+L63 lambs), and (iii) all the pairwise treatment groups (L0 from L21 lambs, L0 from L42 lambs, L0 from 63 lambs, L21 from L42 lambs, L21 from L63 lambs, and L42 from L63 lambs). As the classes were not balanced in the discrimination between stall-fed lambs (L0 lambs) and lucerne pasture-finished lambs (L21+L42+L63 lambs), the down-sampling method of the caret package was used prior to PLS-DA: it randomly sampled the larger group to make it the same size as the smaller group. The effect of the year was not taken into account in the PLS-DA, as the objective is for the authentication method to be reliable whatever the year (Dian et al., 2008; Huang et al., 2015; Rey-Cadilhac et al., 2024).

For each tissue, when the proportion of lambs correctly classified in the following pairwise discriminations (L0 from L21 lambs, L0 from L42 lambs, and L0 from L63 lambs) increased significantly (assessed by a Fisher's exact test followed by a post hoc test with Benjamini-Hochberg correction), then we considered that the pasture signature in the Vis-NIR spectrum of adipose tissue had appeared in a higher proportion of lambs (Rey-Cadilhac et al., 2024). This gave an indication on the time of appearance of the pasture signature within the adipose tissue. When the proportion of lambs correctly classified and misclassified in the following discriminations (L21 from L42 lambs and L42 from L63 lambs) was not different from a random draw (assessed by a Chi-square goodness-of-fit test), then we considered that the pasture signature in the Vis-NIR spectrum of adipose tissues had stabilized (Rey-Cadilhac et al., 2024).

# 3. Results and discussion

## 3.1. Animal performances and carcass characteristics
Four lambs were removed from the trial due to injury or sickness, and six lambs died during the trial. LW at the beginning of the grazing period was 37.3 kg, 31.9 kg, and 27.0 kg for L21, L42 and L63 lambs, respectively. LW gain at pasture was 0 kg, 7.8 kg and 15.3 kg for L21, L42 and L63 lambs, respectively (Table 1). LW change within the 3 weeks following turn-out to pasture decreased from L63 at 4.1 kg to L42 at 1.4 kg down to L21 at +0 kg, with high intra-group variability between animals (Table 1). This result was commented in Prache et al. (2023): the abrupt shift from concentrate-based diet indoors to lucerne pasture diet outdoors without any concentrate, which was dictated by the objectives of the study, particularly stressed the L21 lambs and to a lesser extent the L42 lambs. Despite being co-grazed, the lambs still showed unpredictable variability in individual performances. This variability led to a lower degree of carcass fatness in L21 and L42 lambs compared to L0 and L63 lambs (P < 0.001; Table 1) but no significant difference between L21 and L42 lambs or between L0 and L63 lambs. L21 and L42 lambs also had a lower carcass weight than L63 lambs, the other pairwise comparisons being not significant. PAT weight was significantly lower in L21 and L42 lambs than L0 lambs (P ≤ 0.01), but was not significantly different between L0 and L63 lambs or between L42 and L63 lambs. These results can be explained by average daily gain between birth and slaughter and LW at slaughter, which were lower for L21 lambs than L0 and L63 lambs (P ≤ 0.01). L42 lambs showed intermediate performances, with no difference compared to L0 and L21 lambs but lower average daily gain and LW at slaughter compared to L63 lambs (P ≤ 0.05).

## 3.2. Discrimination of the four treatment groups (L0 vs L21 vs L42 vs L63 lambs)

The Vis-NIR spectroscopy on PAT and CAT combined with PLS-DA showed a low level of accuracy for discriminating the four treatment groups, with 61.7% and 42.2% lambs correctly classified, respectively (Table 2). Based on the results of the permutation test, these results were not significantly different from a random assignment of lambs to the treatment-groups for PAT (P > 0.05 for AUROC, accuracy and class error rate, Table 2) and CAT (P > 0.05 for AUROC, accuracy and class error rate, Table 2).

This accuracy level was far below that reported by Huang et al. (2015) who correctly classified 99.0% of lambs when using the PAT Vis-NIR spectrum to discriminate pasture-finished lambs vs stall-fed lambs vs lambs concentrate-finished indoors for 28 days after pasture-feeding. This difference in accuracy can be at least partly explained by: i) the more contrasted feeding treatments in Huang et al. (2015) (lambs finished on pasture for 101 days on average, stall-fed lambs, and lambs concentrate-finished indoors
for 28 days after pasture-feeding for 84 days on average) than in our study. Indeed, in our study,
treatment groups differed by only 21 days of finishing on pasture (0, 21, 42 and 63 days of pasture
finishing for groups L0, L21, L42, L63); and ii) the higher number of lambs per treatment group (69, 55
and 65 lambs in the 3 groups) in Huang et al. (2015).

The L0 lambs were better discriminated than the other treatment groups for both adipose tissues, with
0.0% L0 lambs misclassified for both PAT and CAT, respectively, between 56.3% and 90.9% of L21 and
L42 lambs misclassified for both PAT and CAT, and 28.6% and 63.6% of L63 lambs misclassified for
PAT and CAT, respectively (Table 3). These results encouraged us to further analyze the discrimination
between stall-fed lambs (L0) and lucerne pasture-finished lambs (L21+L42+L63) (this point is addressed
in paragraph 3.3.).

The regions of the spectrum that contributed to the discrimination of the four treatment groups (VIP
scores ≥ 1.0) were notably situated in the visible region between 400 and 680-720 nm for both PAT and
CAT (Figure S3). This region, where carotenoid and haem pigments absorb light, has already been
identified as important for pasture-feeding authentication in previous studies (Dian et al., 2008; Huang
et al., 2015; Rey-Cadilhac et al., 2024). The other regions of the spectrum that contributed the most to
the discrimination for PAT were situated between 740 and 760 nm, 850 and 870 nm, around 980 nm,
between 1090 and 1220 nm, around 1330 nm, 1370 nm, 1460 nm, and 1540 nm, between 1690 and
1820 nm, around 1930 nm, between 2110 and 2180 and between 2240 and 2470 nm (Figure S3). The
other regions of the spectrum that contributed the most to the discrimination for CAT were situated
between 960 and 1000 nm, 1100 and 1120 nm, 1310 and 1350 nm, 1590 and 1610 nm, 1890 and 2010
nm, and 2290 and 2420 nm (Figure S3). Some of these regions identified in the NIR region could indicate
the implication of molecular groups related to fatty acids, as suggested by Huang et al. (2015) and Dian
et al. (2008). Indeed, fatty acids absorb light in these regions of the spectra (Giaretta et al., 2019; Parrini
et al., 2023).

Using data from years 1 and 2 of this trial, Rey-Cadilhac et al. (2024) showed that discrimination of the
four treatment groups based on PLS-DA on the PAT visible spectrum yielded an accuracy of 75.2%
lambs correctly classified. When performing the PLS-DA on the PAT Vis-NIR spectrum with the data of
these two years, accuracy reached 64.1% of lambs correctly classified. In this trial, the discrimination of
the four treatment groups based on the PAT spectrum was thus more accurate using visible
spectroscopy (75.2% of lambs correctly classified) than Vis-NIR spectroscopy. This unexpected
outcome contrasts with the findings of Huang et al. (2015): these authors obtained a better discrimination
between pasture-fed lambs vs stall-fed lambs vs lambs concentrate-finished indoors for 28 days after
pasture-feeding when using the PAT Vis-NIR spectrum than when using the PAT visible spectrum
(99.0% vs 95.1% lambs correctly classified, respectively). As mentioned above, this result could be
explained by the longer grazing duration used in Huang et al. (2015) (101 and 84 days on average for
pasture-fed lambs and lambs concentrate-finished after pasture feeding, respectively) leading to greater
between-treatment-group contrasts in the optical properties of the adipose tissue in the NIR region of
the spectrum. Differences in the optical properties of adipose tissue may notably result from differences
in its fatty acid composition. It has been shown, for example, that omega 3 fatty acid content is higher
in adipose tissues of pasture-fed lambs compared to stall-fed lambs (Aurousseau et al., 2004; Gruffat
et al., 2020; Ke et al., 2023; Nuernberg et al., 2005) and decreases when pasture-fed lambs are stall-
finished with a concentrate-based diet (Aurousseau et al., 2007). The change in omega 3 fatty acid
content with increasing pasture-finishing durations before slaughter has not been studied in lambs, but
in beef, Noci et al. (2005) observed a linear increase with pasture-finishing durations from 0 to 158 days.
Consequently, the longer grazing duration in Huang et al. (2015) could have led to greater between
treatment-groups differences in PAT fatty acid composition, and therefore to a better discrimination
when using the PAT Vis-NIR spectrum compared to here.

3.3. Discrimination of stall-fed lambs vs lucerne pasture-finished lambs (L0 lambs vs L21+L42+L63 lambs)

Vis-NIR spectroscopy on PAT or CAT combined with PLS-DA to discriminate stall-fed lambs vs lucerne pasture-finished lambs showed an accuracy of 96.9% and 87.5% of lambs correctly classified, respectively (Table 2). The stall-fed lambs were slightly better discriminated than the lucerne pasture-finished lambs with 0.0% and 8.3% of stall-fed lambs misclassified and 6.3% and 16.7% of lucerne pasture-finished lambs misclassified for PAT and CAT, respectively (Table 4). Permutation tests confirmed that these results were significant for both adipose tissues (P = 0.01 for AUROC, accuracy and class error; Table 2 and Figure 3). Previous studies using the PAT Vis-NIR spectrum to discriminate stall-fed lambs from pasture-fed lambs obtained a similar accuracy, with 100% (Huang et al., 2015) and 97.7% (Dian et al., 2008) of lambs correctly classified.
The regions of the spectrum that contributed the most to the discrimination for PAT were situated between 400 and 620 nm, 1090 and 1270 nm, 1330 and 1380 nm, 1450 and 1500 nm, 1530 and 1570 nm, 1680 and 1830 nm, 2110 and 2180 nm, and between 2240 and 2470 nm (Figure 4A). The regions of the spectrum that contributed the most to the discrimination for CAT were situated between 400 to 670 nm, around 790 nm, 980 nm, 1160 nm, 1200 nm, 1330 nm, and 1370 nm, between 1450 and 1580 nm, 1700 and 1800 nm, 1910 and 1950 nm, 2120 to 2180 nm, and between 2240 and 2470 nm (Figure 4B). As discussed earlier, the VIP scores confirmed that the visible region (400 to 800 nm) was of great importance for the discrimination of stall-fed from lucerne pasture-finished lambs and that molecular groups related to fatty acids absorbing in the NIR region could be involved in the discrimination, as already suggested by Huang et al. (2015) and Dian et al. (2008).

Using data from years 1 and 2 of this trial, Rey-Cadilhac et al. (2024) showed that the discrimination based on PLS-DA on PAT visible spectrum yielded an accuracy of 96.5% lambs correctly classified. When performing the PLS-DA on PAT Vis-NIR spectrum with the data from these two years, the accuracy reached 95.0% of lambs correctly classified. The discrimination of stall-fed lambs from lucerne pasture-finished lambs was thus as accurate with PAT visible spectroscopy as with PAT Vis-NIR spectroscopy. Note that Prache et al. (2018) demonstrated that the Romane breed used in this study was the breed (among three breeds: Romane, Ile-de-France and Limousine) that gave the best discrimination between pasture-fed and stall-fed lambs using PAT visible spectroscopy (Prache et al., 2018), due to its greater ability to absorb and store carotenoids in adipose tissues (Macari et al., 2017).

**3.4. Time of appearance and stabilization of the pasture signature within Vis-NIR adipose tissue spectrum**

The accuracy of the discrimination between L0 lambs and L21 lambs (90.3% and 78.3% of lambs correctly classified for PAT and CAT, respectively) was significantly lower than the accuracy of the discrimination between L0 lambs and L63 lambs (100.0% and 95.7% of lambs correctly classified for PAT and CAT, respectively) (Table 5, *P* = 0.005 and *P* < 0.001) but did not differ from the accuracy of the discrimination between L0 lambs and L42 lambs (*P* = 0.435 and *P* < 0.136, respectively). For PAT, the accuracy of discrimination between L0 lambs and L42 lambs (93.8% of lambs correctly classified) was also significantly lower than the accuracy of the discrimination between L0 and L63 lambs (100.0% of lambs correctly classified) (*P* = 0.029). These results indicated that the pasture signature was
established in the PAT and CAT spectra of most (more than 95%) lambs between 0 and 63 days and
had already appeared in those between 0 and 21 days for 90.3% and 78.3% of lambs, respectively. In
a previous paper, Rey-Cadilhac et al. (2024) observed that the pasture signature appeared for most
lambs (more than 94%) between 0 and 21 days in the PAT and subcutaneous dorsal adipose tissue
visible spectrum. These findings demonstrated that, in this trial, the pasture signature was detectable in
both the visible and Vis-NIR spectra from as early as 21 days at pasture in a high proportion of lambs.
They also suggested that the pasture signature was established slightly later in Vis-NIR than in visible
adipose tissue spectrum for most lambs (> 95% of the lambs). However, further studies, focusing on the
comparison between adipose tissue visible and Vis-NIR spectrum and with a larger database, are
needed to confirm these results.

Stabilization of the pasture signature can be considered acquired if half of the lambs are misclassified
in the pairwise discriminations between contiguous pasture-finished treatment groups, which is
equivalent to random lamb assignment. The proportion of lambs correctly classified reached 70.0% and
68.2% in the discrimination between L21 lambs and L42 lambs and 73.0% and 59.1% in the
discrimination between L42 lambs and L63 lambs for PAT and CAT (Table 5), respectively. This
proportion did not reached 50% for PAT (P < 0.001), while it was not significantly different from 50% in
the discrimination between L42 and L63 lambs for CAT (P = 0.069). On this basis, the pasture signature
was not stabilized in PAT for the range of lucerne grazing durations explored in this study (0-63 days),
while it stabilized between 42 and 63 days pasture-finishing in CAT. In the future, to test longer pasture-
finishing durations would be interesting to better understand the timing of pasture signature stabilization
in different animal tissues. Note that, given that the slaughter age was approximately the same for all
lambs, the difference of feeding management between the treatment groups in this study were a balance
between a period of concentrate-feeding and a period of pasture-finishing. Note too that measurement
of visible-NIR spectra was carried out with laboratory-based equipment on pre-processed adipose
tissues (ground samples) to optimize the accuracy and repeatability of the spectral measures.
Equipment for measuring visible-NIR spectra directly on carcasses in the slaughterhouse is currently
being developed to enable this tool to be applied routinely.

4. Conclusion
PLS-DA applied on the Vis-NIR spectrum of PAT and CAT failed to reliably discriminate the four treatment groups and therefore to reliably authenticate lucerne pasture-finishing duration before slaughter. However, it was able to discriminate stall-fed lambs from lucerne pasture-finished lambs with an accuracy of 96.9% and 87.50% lambs correctly classified based on PAT and CAT spectra, respectively. Vis-NIR spectroscopy was as accurate as visible spectroscopy in discriminating between pasture-finished and stall-fed lambs. In this study, the lucerne pasture signature appeared in the PAT and CAT Vis-NIR spectrum in most lambs (more than 95%) between 0 and 63 days of lucerne-pasture finishing; it stabilized between 42 and 63 days of lucerne-pasture finishing in CAT, but had not stabilized in PAT for the range of lucerne grazing durations pre-slaughter explored in this study. To develop a full picture of the time to appearance and stabilization of the lucerne pasture signature in the animal tissues, further research is needed to test a wider range of lucerne pasture-finishing durations before slaughter.
In addition, there is a need for external validation of the current models, using new data.

**Conflict of interests**

None to declare.

**Acknowledgements**

The authors acknowledge L. Lavelle, C. Coustet and the staff of the Herbipôle Experimental Unit of INRAE, and S. Faure and C. Labonne of the UMR Herbivores for their technical support, as well as A. Imbert of the UMR Herbivores for her statistical advices. We also thank V. Bellet of the Institut de l'Elevage and C. Experton and S. Rouger of the Institut Technique de l'Agriculture Biologique for the implementation and coordination of the REVABIO project.

**Funding**

This work was supported by the Special Assignment Account for "Agricultural and Rural Development" (CASDAR) of the French Ministry of Agriculture, Agrifood and Forestry, under project 19AIP5912-REVABIO.

**Authors ORCIDs**

L. Rey-Cadilhac: https://orcid.org/0000-0002-4554-7662
## Authors contributions

L. Rey-Cadilhac: software, investigation, statistical analysis, writing original draft

D. Andueza: methodology, software, investigation, statistical analysis, review and editing

S. Prache: conceptualization, investigation, review and editing, project administration, funding acquisition, supervision.

All authors have read and agreed to the manuscript.

## References

Aurousseau, B., Bauchart, D., Calichon, E., Micol, D., & Priolo, A. (2004). Effect of grass or concentrate feeding systems and rate of growth on triglyceride and phospholipid and their fatty acids in the M. longissimus thoracis of lambs. Meat Science, 66(3), 531-541. https://doi.org/10.1016/S0309-1740(03)00156-6

Aurousseau, B., Bauchart, D., Faure, X., Galot, A. L., Prache, S., Micol, D., & Priolo, A. (2007). Indoor fattening of lambs raised on pasture. Part 1: Influence of stall finishing duration on lipid classes and fatty acids in the longissimus thoracis muscle. Meat Science, 76(2), 241-252. https://doi.org/10.1016/j.meatsci.2006.11.005

Barnes, R. J., Dhanoa, M. S., & Lister, S. J. (1989). Standard Normal Variate Transformation and De-Trending of Near-Infrared Diffuse Reflectance Spectra. Applied Spectroscopy, 43(5), 772-777. https://doi.org/10.1366/0003702894202201

Chong, I.-G., & Jun, C.-H. (2005). Performance of some variable selection methods when multicollinearity is present. Chemometrics and Intelligent Laboratory Systems, 78(1), 103-112. https://doi.org/10.1016/j.chemolab.2004.12.011

Delwiche, S.R., Reeves, J.B. (2004). The effect of spectral pre-treatments on the partial least squares modelling of agricultural products. Journal of Near Infrared Spectroscopy, 12, 177-182.

Devincenzi, T., Prunier, A., Meteau, K., & Prache, S. (2019). How does barley supplementation in lambs grazing alfalfa affect meat sensory quality and authentication? Animal, 13(2), 427-434. https://doi.org/10.1017/S1751731118001477
Dian, P. H. M., Andueza, D., Barbosa, C. M. P., Amoureux, S., Jestin, M., Carvalho, P. C. F., Prado, I. N., & Prache, S. (2007). Methodological developments in the use of visible reflectance spectroscopy for discriminating pasture-fed from concentrate-fed lamb carcasses. *Animal, 1*(8), 1198-1208. https://doi.org/10.1017/S175173110700047X

Dian, P. H. M., Andueza, D., Jestin, M., Prado, I. N., & Prache, S. (2008). Comparison of visible and near infrared reflectance spectroscopy to discriminate between pasture-fed and concentrate-fed lamb carcasses. *Meat Science, 80*(4), 1157-1164. https://doi.org/10.1016/j.meatsci.2008.05.009

Eiras, C. E., Prunier, A., Meteau, K., do Prado, I. N., & Prache, S. (2022). Is a short concentrate-finishing period in lambs raised on alfalfa pasture effective at reducing fat indoles contents and lightening meat colour? *Animal, 16*(8), 100610. https://doi.org/10.1016/j.animal.2022.100610

Garrido-Varo, A., Garcia-Olmo, J., & Fearn, T. (2019). A note on Mahalanobis and related distance measures in WinISI and The Unscrambler. *Journal of Near Infrared Spectroscopy, 27*(4), 253-258. https://doi.org/10.1364/JNIRS.27.000253

Giaretta, E., Mordenti, A., Palmonari, A., Brogna, N., Canestrari, G., Belloni, P., Cavallini, D., Mammi, L., Cabbri, R., & Formigoni, A. (2019). NIRs calibration models for chemical composition and fatty acid families of raw and freeze-dried beef: A comparison. *Journal of Food Composition and Analysis, 83*, 103257. https://doi.org/10.1016/j.jfca.2019.103257

Gruffat, D., Durand, D., Rivaroli, D., do Prado, I. N., & Prache, S. (2020). Comparison of muscle fatty acid composition and lipid stability in lambs stall-fed or pasture-fed alfalfa with or without sainfoin pellet supplementation. *Animal, 14*(5), 1093-1101. https://doi.org/10.1017/S1751731119002507

Guy, F., Prache, S., Thomas, A., Bauchart, D., Andueza, D. (2011). Prediction of lamb meat fatty acid composition using near-infrared reflectance spectroscopy (NIRS). *Food Chemistry, 127*, 1280-1286. https://doi.org/10.1016/j.foodchem.2011.01.084

Huang, Y., Andueza, D., de Oliveira, L., Zawadzki, F., & Prache, S. (2015). Comparison of visible and near infrared reflectance spectroscopy on fat to authenticate dietary history of lambs. *Animal, 9*(11), 1912-1920. https://doi.org/10.1017/S1751731115001172
Ke, T., Zhao, M., Zhang, X., Cheng, Y., Sun, Y., Wang, P., Ren, C., Cheng, X., Zhang, Z., & Huang, Y. (2023). Review of feeding systems affecting production, carcass attributes, and meat quality of ovine and caprine species. Life, 13(5), 1215. https://doi.org/10.3390/life13051215

Leys, C., & Schumann, S. (2010). A nonparametric method to analyze interactions: The adjusted rank transform test. *Journal of Experimental Social Psychology*, 46(4), 684-688.
https://doi.org/10.1016/j.jesp.2010.02.007

Logan, B. G., Hopkins, D. L., Schmidtke, L. M., & Fowler, S. M. (2021). Authenticating common Australian beef production systems using Raman spectroscopy. Food Control, 121, 107652.
https://doi.org/10.1016/j.foodcont.2020.107652

Macari, S., Graulet, B., Andueza, D., & Prache, S. (2017). Nitrogen stable isotope and carotenoid pigments signatures in the meat as tools to trace back the diet: Comparison between two sheep breeds. Small Ruminant Research, 153, 107-113.
https://doi.org/10.1016/j.smallrumres.2017.05.013

Monahan, F. J., Schmidt, O., & Moloney, A. P. (2018). Meat provenance: Authentication of geographical origin and dietary background of meat. Meat Science, 144, 2-14.
https://doi.org/10.1016/j.meatsci.2018.05.008

Noci, F., Monahan, F. J., French, P., & Moloney, A. P. (2005). The fatty acid composition of muscle fat and subcutaneous adipose tissue of pasture-fed beef heifers: Influence of the duration of grazing. Journal of Animal Science, 83(5), 1167-1178. https://doi.org/10.2527/2005.8351167x

Nuernberg, K., Nuernberg, G., Ender, K., Dannenberger, D., Schabbel, W., Grumbach, S., Zupp, W., & Steinhart, H. (2005). Effect of grass vs. Concentrate feeding on the fatty acid profile of different fat depots in lambs. European Journal of Lipid Science and Technology, 107(10), 737-745.
https://doi.org/10.1002/ejlt.200501141

Parrini, S., Sirtori, F., Čandek-Potokar, M., Charneca, R., Crovetti, A., Kušec, I. D., Sanchez, E. G., Cebrian, M. M. I., Garcia, A. H., Karolyi, D., Lebret, B., Ortiz, A., Panella-Riera, N., Petig, M., Jesus da Costa Pires, P., Tejerina, D., Razmaite, V., Aquilani, C., & Bozzi, R. (2023). Prediction of fatty acid composition in intact and minced fat of European autochthonous pigs breeds by near infrared spectroscopy. Scientific Reports, 13(1), 7874. https://doi.org/10.1038/s41598-023-34996-x
Prache, S., Huang, Y., & Andueza, D. (2018). To what extent is a breed-specific database necessary to differentiate meat from pasture-fed and stall-fed lambs using visible spectroscopy? *Animal*, 12(8), 1682-1689. https://doi.org/10.1017/S1751731117003366

Prache, S., Martin, B., & Coppa, M. (2020). Review: Authentication of grass-fed meat and dairy products from cattle and sheep. *Animal*, 14(4), Art. 4. https://doi.org/10.1017/S1751731119002568

Prache, S., Rey-Cadilhac, L., & Prunier, A. (2023). Change in fat skatole and indole content in lambs switched from a concentrate-based diet indoors to alfalfa grazing for various durations before slaughter. *Meat Science*, 204, 109272. https://doi.org/10.1016/j.meatsci.2023.109272

Prache, S., & Thériez, M. (1999). Traceability of lamb production systems: Carotenoids in plasma and adipose tissue. *Animal Science*, 69(1), 29-36. https://doi.org/10.1017/S1357729800051067

Rey-Cadilhac, L., Andueza, D., & Prache, S. (2024). Visible spectroscopy on lamb fat and muscle to authenticate the duration of pasture finishing. *Meat Science*, 208, 109377. https://doi.org/10.1016/j.meatsci.2023.109377

Rivaroli, D., Prunier, A., Meteau, K., do Prado, I. N., & Prache, S. (2019). Tannin-rich sainfoin pellet supplementation reduces fat volatile indoles content and delays digestive parasitism in lambs grazing alfalfa. *Animal*, 13(9), 1883-1890. https://doi.org/10.1017/S1751731118003543

Seo, S., Jeon, S., & Ha, J. K. (2018). —Editorial— Guidelines for experimental design and statistical analyses in animal studies. *Asian-Australasian Journal of Animal Sciences*, 31(9), Article 9. https://doi.org/10.5713/ajas.18.0468

Shenk, J.S., Westerhaus, M.O. (1994). The application of near infrared reflectance spectroscopy (NIRS) to forage analysis, Forage Quality, Evaluation, and Utilization. John Wiley & Sons, Ltd, 406–449. https://doi.org/10.2134/1994.foragequality.c10

Szymańska, E., Saccenti, E., Smilde, A. K., & Westerhuis, J. A. (2012). Double-check: Validation of diagnostic statistics for PLS-DA models in metabolomics studies. *Metabolomics*, 8(Suppl 1), 3-16. https://doi.org/10.1007/s11306-011-0330-3

Wobbrock, J. O., Findlater, L., Gergle, D., & Higgins, J. J. (2011). The aligned rank transform for nonparametric factorial analyses using only anova procedures. *Proceedings of the SIGCHI Conference on Human Factors in Computing Systems*, 143-146. https://doi.org/10.1145/1978942.1978963
## Tables

<table><caption>Table 1: Performances and carcass characteristics of lambs pasture-finished on lucerne for different durations before slaughter</caption><thead><tr><th rowspan="2">Characteristic</th><th colspan="4">Treatment group<sup>1,2</sup></th><th rowspan="2">SEM<sup>3</sup></th><th rowspan="2">P-value<sup>4</sup></th></tr><tr><th>L0</th><th>L21</th><th>L42</th><th>L63</th></tr></thead><tbody><tr><td>n</td><td>55</td><td>50</td><td>54</td><td>50</td><td>-</td><td>-</td></tr><tr><td>Average daily gain between birth and slaughter (g/day)</td><td>260ab (145–370)</td><td>234c (153–302)</td><td>246bc (169–328)</td><td>273a (162–341)</td><td>8.9</td><td>&lt;0.001</td></tr><tr><td>LWG at pasture the first 3 weeks (kg)</td><td></td><td>0.0c (-6.0–6.0)</td><td>1.4b (-2.3–4.6)</td><td>4.1a (-2.0–8.2)</td><td>0.4</td><td>&lt;0.001</td></tr><tr><td>Total LWG at pasture (kg)</td><td>-</td><td>0.0c (-6.0–6.0)</td><td>7.8b (-2.0–14.2)</td><td>15.3a (3.1–22.1)</td><td>1.09</td><td>&lt;0.001</td></tr><tr><td>LW at slaughter (kg)</td><td>41.1ab (24.7–61.0)</td><td>37.2c (25.5–50.0)</td><td>40.0bc (22.8–55.8)</td><td>42.6a (24.8–55.0)</td><td>3.16</td><td>&lt;0.001</td></tr><tr><td>Carcass weight (kg)</td><td>18.8ab (9.9–30.0)</td><td>17.4b (11.8–23.5)</td><td>17.9b (9.1–25.3)</td><td>19.6a (10.4–26.2)</td><td>1.56</td><td>=0.001</td></tr><tr><td>Degree of carcass fatness (1–15)</td><td>7.5a (4.0–11)</td><td>6.3b (4.0–9)</td><td>6.4b (4.0–9)</td><td>7.1a (4.0–10)</td><td></td><td>&lt;0.001</td></tr><tr><td>Perirenal adipose tissue weight (g)</td><td>235a (55–778)</td><td>146c (41–355)</td><td>179bc (37–439)</td><td>211ab (24–434)</td><td></td><td>&lt;0.001</td></tr></tbody></table>

<footnote n="1">¹ L0, L21, L42 and L63: lambs finished on lucerne pasture for 0, 21, 42 and 63 days before slaughter, respectively</footnote>

<footnote n="2">²Mean (minimum–maximum) of the raw data</footnote>

<footnote n="3">³SEM: Standard error of the mean; no SEM is given when we log-transformed the variable or when we used non-parametric tests.</footnote>

<footnote n="4">⁴Within a row, means with different letters are significantly different at $P \le 0.05$</footnote>
<table><caption>Table 2: Accuracy and parameters of partial least squares-discriminant analysis (PLS-DA) applied on the visible-NIR spectrum of lamb perirenal and caudal adipose tissue to discriminate (a) between the four treatment groups (L0, L21, L42, L63) or (b) between stall-fed lambs and lucerne pasture-finished lambs</caption><thead><tr><th rowspan="2">Discrimination<sup>1</sup></th><th rowspan="2">Tissue</th><th rowspan="2">Accuracy (%)</th><th colspan="3">PLS-DA parameters<sup>2</sup></th></tr><tr><th>Spectrum pre-processing</th><th>LV</th><th>Permutation test (P-value<sub>Accuracy</sub>)</th></tr></thead><tbody><tr><td>(a) L0 vs L21 vs L42 vs L63</td><td>Perirenal adipose tissue</td><td>61.7</td><td>SNV-D/1<sup>st</sup>D</td><td>8</td><td>1.00</td></tr><tr><td>(b) L0 vs L21+L42+L63</td><td>Caudal adipose tissue</td><td>42.2</td><td>No</td><td>8</td><td>1.00</td></tr><tr><td></td><td>Perirenal adipose tissue</td><td>96.9</td><td>SNV-D/1<sup>st</sup>D</td><td>6</td><td>0.01</td></tr><tr><td></td><td>Caudal adipose tissue</td><td>87.5</td><td>SNV-D/1<sup>st</sup>D</td><td>4</td><td>0.01</td></tr></tbody></table>

<footnote n="1">¹ L0, L21, L42 and L63: lambs finished on lucerne pasture for 0, 21, 42 and 63 days before slaughter, respectively</footnote>

<footnote n="2">²SNV-D: standard normal variate and detrend; 1stD: first derivative; LV: latent variables in the PLS-DA model</footnote>
<table><caption>Table 3: Model performances for discrimination of lambs from the four treatment groups (L0, L21, L42, L63) using partial least squares-discriminant analysis (PLS-DA) applied on the visible-NIR spectrum of perirenal adipose tissue and caudal adipose tissue</caption><thead><tr><th rowspan="2">Performance parameters<sup>2</sup></th><th colspan="4">Perirenal adipose tissue<sup>1</sup></th><th colspan="4">Caudal adipose tissue<sup>1</sup></th></tr><tr><th>L0</th><th>L21</th><th>L42</th><th>L63</th><th>L0</th><th>L21</th><th>L42</th><th>L63</th></tr></thead><tbody><tr><td>Class error (%)</td><td>0.0</td><td>71.4</td><td>56.3</td><td>28.6</td><td>0.0</td><td>81.8</td><td>90.9</td><td>63.6</td></tr><tr><td>Sensitivity (%)</td><td>100.0</td><td>28.6</td><td>43.8</td><td>71.4</td><td>100.0</td><td>18.2</td><td>9.1</td><td>36.4</td></tr><tr><td>Specificity (%)</td><td>77.3</td><td>95.7</td><td>88.6</td><td>87.0</td><td>81.8</td><td>97.1</td><td>79.4</td><td>64.7</td></tr><tr><td>FNR (%)</td><td>0.0</td><td>71.4</td><td>56.3</td><td>28.6</td><td>0.0</td><td>81.8</td><td>90.9</td><td>63.6</td></tr><tr><td>FPR (%)</td><td>22.7</td><td>4.3</td><td>11.4</td><td>13.0</td><td>18.2</td><td>2.9</td><td>20.6</td><td>35.3</td></tr></tbody></table>

before slaughter, respectively

<footnote n="1">¹ L0, L21, L42 and L63: lambs finished on lucerne pasture for 0, 21, 42 and 63 days</footnote>

<footnote n="2">²FNR: false negative rate; FPR: false positive rate</footnote>
<table><caption>Table 4: Model performances for the discrimination of stall-fed lambs from lucerne pasture-finished lambs using partial least squares-discriminant analysis (PLS-DA) applied on the visible-NIR spectrum of perirenal adipose tissue and caudal adipose tissues</caption><thead><tr><th rowspan="2">Performance parameters<sup>2</sup></th><th colspan="2">Perirenal adipose tissue<sup>1</sup></th><th colspan="2">Caudal adipose tissue<sup>1</sup></th></tr><tr><th>L0</th><th>L21+L42+L63</th><th>L0</th><th>L21+L42+L63</th></tr></thead><tbody><tr><td>Class error (%)</td><td>0.0</td><td>6.3</td><td>8.3</td><td>16.7</td></tr><tr><td>Sensitivity (%)</td><td>100.0</td><td>93.8</td><td>91.7</td><td>83.3</td></tr><tr><td>Specificity (%)</td><td>93.8</td><td>100.0</td><td>83.3</td><td>91.7</td></tr><tr><td>FNR (%)</td><td>0.0</td><td>6.3</td><td>8.3</td><td>16.7</td></tr><tr><td>FPR (%)</td><td>6.3</td><td>0.0</td><td>16.7</td><td>8.3</td></tr></tbody></table>

<footnote n="1">¹ L0, L21, L42 and L63: lambs finished on lucerne pasture for 0, 21, 42 and 63 days before slaughter, respectively</footnote>

<footnote n="2">² FNR: false negative rate; FPR: false positive rate</footnote>
<table><caption>Table 5: Accuracy and parameters of partial least squares-discriminant analysis (PLS-DA) applied on the visible-NIR spectra of lamb perirenal adipose tissue and caudal adipose tissue to discriminate between treatment groups (L0, L21, L42, L63) by pairs</caption><thead><tr><th rowspan="2">Tissue</th><th rowspan="2">Discrimination<sup>1</sup></th><th rowspan="2">Accuracy (%)</th><th colspan="2">PLS-DA parameters<sup>2</sup></th></tr><tr><th>Spectrum pre-processing</th><th>LV</th></tr></thead><tbody><tr><td rowspan="6">Perirenal adipose tissue</td><td>L0 vs L21</td><td>90.3</td><td>No</td><td>7</td></tr><tr><td>L0 vs L42</td><td>93.8</td><td>SNV-D/1<sup>st</sup>D</td><td>5</td></tr><tr><td>L0 vs L63</td><td>100.0</td><td>SNV-D/1<sup>st</sup>D</td><td>6</td></tr><tr><td>L21 vs L42</td><td>70.0</td><td>SNV-D/1<sup>st</sup>D</td><td>5</td></tr><tr><td>L21 vs L63</td><td>78.6</td><td>SNV-D/1<sup>st</sup>D</td><td>5</td></tr><tr><td>L42 vs L63</td><td>70.0</td><td>SNV-D/1<sup>st</sup>D</td><td>5</td></tr><tr><td rowspan="6">Caudal adipose tissue</td><td>L0 vs L21</td><td>78.3</td><td>No</td><td>8</td></tr><tr><td>L0 vs L42</td><td>87.0</td><td>SNV-D/1<sup>st</sup>D</td><td>5</td></tr><tr><td>L0 vs L63</td><td>95.7</td><td>No</td><td>9</td></tr><tr><td>L21 vs L42</td><td>68.2</td><td>SNV-D/1<sup>st</sup>D</td><td>8</td></tr><tr><td>L21 vs L63</td><td>81.8</td><td>SNV-D/1<sup>st</sup>D</td><td>6</td></tr><tr><td>L42 vs L63</td><td>59.1</td><td>No</td><td>5</td></tr></tbody></table>

<footnote n="1">¹ L0, L21, L42 and L63: lambs finished on lucerne pasture for 0, 21, 42 and 63 days before slaughter, respectively</footnote>

<footnote n="2">²SNV-D: standard normal variate and detrend; 1stD: first derivative;</footnote>

<footnote>LV: latent variables in the PLS-DA model</footnote>
## Figures

<caption>Figure 1: Raw mean Vis-NIR absorbance spectra of lamb perirenal fat (A) and caudal fat (B).</caption>

L0, L21, L42 and L63: lambs finished on lucerne pasture for 0, 21, 42 and 63 days before slaughter, respectively

<figure><img src="image_1.png" /></figure>
<figure><img src="image_2.png" /><figcaption>Figure 2: Pre-processed mean Vis-NIR absorbance spectra of lamb perirenal fat (A) and caudal fat (B). L0, L21, L42 and L63: lambs finished on lucerne pasture for 0, 21, 42 and 63 days before slaughter, respectively</figcaption></figure>
Figure 3: Distribution of the performance parameters (A: AUROC; B: Accuracy (%); C: Class error rate (%)) of the permutation tests compared to performance of the model with initial data (blue line) for partial least squares-discriminant analysis (PLS-DA) applied on the (1) perirenal adipose tissue and (2) caudal adipose tissue visible-NIR spectrum to discriminate stall-fed lambs (L0 lambs) from lucerne pasture-finished lambs (L21+L42+L63 lambs).

L0, L21, L42 and L63: lambs finished on lucerne pasture for 0, 21, 42 and 63 days before slaughter, respectively

<figure><img src="image_3.png" /><figcaption>A2</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>B1</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>B2</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>C1</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>C2</figcaption></figure>

<figure><img src="image_8.png" /></figure>
Figure 4: Variable importance for the projection (VIP) scores of the partial least squares-discriminant analysis (PLS-DA) applied on visible-NIR spectra of (A) perirenal adipose tissue and (B) caudal adipose tissue for the discrimination of stall-fed lambs from lucerne pasture-finished lambs.

Wavelengths for which VIP score is ≥ 1 are highlighted in grey

<figure><img src="image_9.png" /></figure>