One option is to interpolate to a finer ‘x’ vecttor, and then use a method such as 'pchip', 'spline', 'makima', or others to do a smoother interpolation.
Example —
x=[0:30:210];
%Composition
%Outlet Specification [PFAD(4)]
y=[44.0789 62.428 77.24324 85.35 89.435 91.353 92.504 92.6252];%FFA
z=[90.4422 59.243 32.241 13.8 5.232 1.2421 0.6243 0.5465];%TAG
figure
plot(x,y,'-b',x,z,'--r')
title('composition vs time');
xlabel('time');
ylabel('xd');
legend('a) FFA','b)TAG');
xv = linspace(min(x), max(x), numel(x)*10);
yv = interp1(x, y, xv,'pchip');
zv = interp1(x, z, xv,'pchip')
figure
plot(xv,yv,'-b',xv,zv,'--r')
title('composition vs time');
xlabel('time');
ylabel('xd');
legend('a) FFA','b)TAG');
.
